Analysis system and methods of use thereof

ABSTRACT

The present disclosure provides for analysis systems that are configured to extract a fluid sample from a fluid (e.g., aqueous solution) in a reactor (e.g., bioreactor) at a first rate and then flow the fluid sample to a sensor system at a second rate to analyze the fluid sample. The sensor system can detect the presence and/or concentration of molecules (e.g., biomolecules such as biomarkers (e.g., metabolites, proteins, peptides, cytokines, growth factors, DNA, RNA, lipids) and cells of different types and cell properties, e.g., mechanical stiffness, etc.)). The data obtained can be used by a feedback control system to modify, as needed, the conditions in the reactor to enhance the productively of the reactor.

CLAIM OF PRIORITY TO RELATED APPLICATION

This application is the 35 U.S.C. § 371 national stage application ofPCT Application entitled “ANALYSIS SYSTEM AND METHODS OF USE THEREOF”having serial no. PCT/US19/46644, filed Aug. 15, 2019, where the PCTclaims priority to, and the benefit of the contents of U.S. provisionalapplication entitled “Multisensor Dynamic Sampling Platform (DSP-X) forContinuous Bioreactor Monitoring and Feedback Control” having Ser. No.62/764,712 filed on Aug. 15, 2018, which are entirely incorporatedherein by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under grant 1648035awarded by the National Science Foundation and under grant GM112662awarded by the National Institutes of Health. The government has certainrights in the invention.

BACKGROUND

Cell therapies and drug developed using them have been shown tosuccessfully treat a range of life threatening illnesses and injuries.In order to enable large scale and cost effective adaption of celltherapies, quality control methodologies and standards for therapeuticcell manufacturing need to be established. During the production oftherapeutic cells, levels of various components used to grow the cellsand cell responses thereto and changes that occur to cells during growthshould be monitored to ensure cell health, efficacy, anddifferentiation. In particular, quality control indicators (QCIs) (e.g.,metabolites, cytokines, and various other proteins and biomolecules)directly related to cell health, efficacy, and differentiation can bemonitored. Monitoring the QCIs is complex due to a wide range ofmolecular weights, different QCIs for different cell types, and a widerange of concentrations. As a result, there is a need to overcome atleast these complexities among others.

SUMMARY

The present disclosure provides for analysis systems that are configuredto extract a fluid sample from a fluid (e.g., aqueous solution) in areactor (e.g., bioreactor) at a first rate and then flow the fluidsample to a sensor system at a second rate to analyze the fluid sample.The sensor system can detect the presence and/or concentration ofmolecules (e.g., biomolecules). The data obtained can be used by afeedback control system to modify, as needed, the conditions in thereactor to enhance the productively of the reactor.

An embodiment of the present disclosure includes a system having a flowsystem that includes a pump system and a valve system, where the pumpsystem and the valve system are in fluidic communication along with asampling system in fluidic communication with the flow system. Thesystem also includes a reactor including a fluid, where the samplingsystem is in fluidic communication with the reactor. In a firstconfiguration of the flow system, the fluid sample is flowed from thereactor to the pump system through the valve system at a first flowrate. A sensor system is in fluidic communication with the flow system.In a second configuration of the valve system, the fluid sample isflowed from the pump system to the sensor system at a second flow rate.The sensor system is configured to analyze the fluid sample. The systemcan also comprise a feedback control system, n-stageseparation/fractionation/trapping system, and/or mass exchanger. Thesystem can further comprise a separation/fractionation/trapping systemin fluidic communication with the flow system and the sampling system,wherein the separation/fractionation/trapping system is configured toseparate a first group of components from the fluid sample to produce aseparated/fractionated fluid sample, wherein the separated/fractionatedfluid sample is analyzed by the sensor system. The system can furthercomprise a mass exchanger in fluidic communication with the flow systemand the sampling system, wherein the mass exchanger is configured tocondition the fluid sample to produce a conditioned fluid sample foranalysis by the sensor system.

An embodiment of the present disclosure provides for a method ofanalyzing a fluid sample comprising: extracting the fluid sample from areactor comprising a fluid, wherein the reactor is in fluidiccommunication with a sampling system; flowing the fluid sample throughthe sampling system to a flow system at a first flow rate, wherein theflow system comprises a pump system and a valve system, wherein the flowsystem in conjunction with the sampling system controls the extractionof the fluid sample from the fluid, wherein the flow system is also influidic communication with a sensor system; and flowing the fluid samplefrom the flow system to the sensor system at a second flow rate, whereinthe sensor system is configured to configured to analyze the fluidsample. The method can further comprise flowing the fluid to aseparation/fractionation/trapping system, wherein theseparation/fractionation/trapping system is configured to separate afirst group of components from the fluid sample. The method can furthercomprise flowing the fluid sample to a mass exchanger, wherein the massexchanger is configured to condition the separated fluid.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the present disclosure can be better understood withreference to the following drawings. The components in the drawings arenot necessarily to scale, with emphasis instead being placed uponclearly illustrating the principles of the disclosure. Moreover, in thedrawings, like reference numerals designate corresponding partsthroughout the several views.

FIGS. 1.1, 1.2A, and 1.2B illustrate embodiments of the analysis system.

FIG. 1.3 illustrate an embodiment of a portion of the analysis system.

FIG. 1.4A-1.4C illustrates a schematic of extracting fluid samples froma reactor.

FIGS. 1.5A-1.5B illustrate a cross-sectional view of embodiments of amass exchanger.

FIGS. 1.6A-1.6B illustrate a cross-sectional view of embodiments of amass exchanger.

FIG. 2.1 is schematic of Dynamic Mass Spectrometry Probe (DMSP)operating principles according to embodiments of the present disclosure.Active sample conditioning through size selective mass exchangersimultaneously removes interferences and infuses ESI-MS enhancingchemicals into a sample through a size selective membrane. Large targetbiomolecules are retained in the sample while allowing smaller moleculesto diffuse freely in/out of the sample.

FIG. 2.2 provides examples of dynamic Mass Spectrometry Probe (DMSP)mass exchanger microfabrication with subsequent integration ofsampling/ESI capillaries according to embodiments of the presentdisclosure.

FIGS. 2.3A-2.3E provide examples of DMSP “active sample conditioning”treatment of 100 mM KCl with 5 uM cytochrome-c (cyt-c). FIG. 2.3A showsuntreated. FIG. 2.3B shows 1% AA treatment reveals cyt-c signal. FIG.2.3C shows that addition of 40 mM ammonium acetate increases signalintensity of cyt-c. FIG. 2.3D shows 2% m-NBA supercharging moleculeshifts the charge state distribution FIG. 2.3E shows 50% methanol in asan active conditioner denatures cyt-c, leading to a similar shift incharge state distribution.

FIGS. 2.4A-2.4C provide examples of enhanced signal to noise ratio (SNR)and limit of detection (LOD) enabled by active sample treatment. FIG.2.4A provides a plot of the highest and 5th highest SNR for 50 mM KClwith 0.25, 0.5, 1, 2.5, 5, and 10 μm cyt-c shows that 2% m-NBA, 1% AAtreatment drastically increases the SNR across all concentrationsexplored, and decreases the LOD (detected molecule>2.5 SNR) from 2.5 to0.25 μM. FIG. 2.4B provides resulting mass spectra of 1% AA treatmentfor 50 mM KCl with 1 μM cyt-c shows no characteristic peaks FIG. 2.4Cshows an addition of 2% m-NBA to active sample treatment and revealsmultiple, fully protonated peaks associated with cyt-c, indicating thepower of DMSP active sample treatment.

FIGS. 2.5A-2.5C demonstrate multicomponent detection enabled by activesample conditioning via DMSP online ESI-MS. FIG. 2.5A shows untreated1×PBS with 5 μm of IL-6, IL-8, and cyt-C shows no characteristic proteinpeaks. FIG. 2.5B shows the sample with 1% AA treatment, heavily saltadducted IL-6 peaks are observed FIG. 2.5C shows active sample treatmentwith 2% m-NBA, fully protonated peaks associated with both cyt-c andIL-6 observed.

FIG. 2.6A shows the relationship between pore size and DMSP percenteffectiveness. By reducing the pore size from the current size of 50 nmto smaller diameters, the device is less “effective” at removingbiomolecules but removes parasitic salt ions just as effectively. Inthis figure, C_(in) and C_(out) represent the mass exchanger inlet andoutlet concentrations of any species. FIG. 2.6B demonstrates thatreducing pore size creates a more size selective device (e.g. lessanalyte loss), but increases the mass transfer resistance associatedwith the membrane, which will reduce the amount of interfering compoundsremoved and increase residence time in DMSP. An optimum pore size designcan be selected based on these trends.

FIG. 2.7 provides an example, according to embodiments of the presentdisclosure, of how rate of mass transfer (i.e. flux, J″) can becalculated based on the total resistance within the DMSP mass exchanger.In this diagram, J″ for the salt/conditioner is always positive and inthe direction of the respective arrow.

FIG. 2.8 shows that with a reduction in any of the three resistances,the total mass transfer resistance and hence residence time within themass exchanger is improved, which results in a better transientresponse. However, reducing the resistance associated with the samplechannel will lead to a higher incidence of clogs. Reducing the thicknessof the membrane, and hence the resistance (assuming pore size is fixed)may lead to membrane structural failure. In the current DMSP design, thelargest resistance is in the conditioner channel, making it the besttarget for immediate optimization efforts.

As shown in FIGS. 2.9A-2.9B, DMSP dynamic sampling interface (DSI)consists of 4 components: 1. A bi-directional, variable flow rate pumpwith sample for uptake/infusion; 2. A switching valve for isolatingsampling/infusion steps; 3. A sampling probe for localized secretomeintake; and 4. interconnecting tubing for sample transport to DMSP. FIG.2.9A shows that sample uptake can be carried out with dynamic pressureprofiles tuned for rapid, low dilution sampling. FIG. 2.9B shows thatduring infusion, constant flow rate will match that necessary for DMSPanalysis.

FIGS. 2.10A-2.10B show that dispersion of a sampled plug reduces theaverage concentration, potentially below the limits of detection for MS.In FIG. 2.10A, as a sampled plug enters a fluidic channel, a nonuniformvelocity disperses the species axially. As the sampled plug advancesthrough the channel, the concentration decreases according to theorydeveloped by Taylor. If the sample is diluted too much, theconcentrations will be below the limit of detection for massspectrometry analysis. In FIG. 2.10B, the relationship is shown betweenmean concentration vs flow rate and effective diffusion coefficient vsflow rate in a 10 cm long, 25 um diameter capillary (representativeresults for illustration only). Low flow rates will result in lowerdispersion rates but higher axial diffusion rates and slow transientresponse. In high flow rate regimes, Taylor dispersion will dilute thespecies at a rate higher than diffusion alone. Higher flow rates alsodecrease transit time from sampling to analysis, hence increasing DMSPtransient response. Optimizing sampling geometry and sampling rates canensure successful transfer of analyte from the bioreactor to the DMSPfor analysis.

FIG. 2.11 provides localized DMSP analysis of 3 types of adherent (2D)cell types. The signatures of each cell type indicate that DMSP iscapable of differentiating between the cells, although additional workis underway to identify specifically which molecules are detected.

FIG. 2.12 provides DMSP treated samples from a 2D cell culture of normalhuman lung fibroblasts in media containing 2% FBS. The similaritiesbetween near samples (first and third) and far (second and fourth)demonstrate that DMSP is capable of differentiating local and bulksamples. Importantly, localized sampling reveals a rich biologicalstate, indicating that there is an increased amount of biomarker QAsfound near cell membranes.

FIG. 3.1 provides representative spectra gathered from time point 6 (endof experiment) for MC3T3 cells. The top six spectra are three bulk andthree local samples from an undifferentiated cell line, while the bottom6 spectra are three bulk and three local samples from a differentiatedcell line. Due to the visual similarities between the spectra,statistical analysis (PCA) was used to identify features mostcontributing to variation between the samples (i.e. quality attributes).

FIGS. 3.2A-3.2B provide principal component analysis (PCA) cluster plotsfor time points 5 and 6 of the undifferentiated cell group vs timepoints 5 and 6 of the differentiated cell group. In FIG. 3.2A, bulksampling reveals minimal clustering. In FIG. 3.2B, localized samplingreveals clusters for the two groups, indicating that localized samplingis beneficial for detecting differences between the cells at each state.

FIGS. 3.3A-3.3B provide principal component analysis (PCA) cluster plotsfor the differentiated cell line at time points 1 and 2 versus timepoints 5 and 6. Cells were expected to begin differentiation betweentime points 3 and 4, with differentiation completed by time point 5. InFIG. 3.3A, bulk sampling does not exhibit any clustering. In FIG. 3.3B,with localized sampling, clusters are observed indicating that DSP candetect differences between undifferentiated and differentiated celllines.

FIG. 3.4 provides a schematic of a prototype ion transfer capillarydesigned for the mass spectrometer. From top left, going counterclockwise: Top view of transfer capillary system, front view, side view,and isometric view. Ions are introduced at the front end of the tubelabeled “ions in”. The vacuum generated draws the ions through the tubeand into the MS inlet.

FIG. 3.5 shows a comparison of direct infusion vs ion transfer infusion.The signal associated with cytochrome-c is completely recovered evenwith the interfacial ion transfer capillary, indicating this technologywill not sacrifice sensitivity when integrated with DSP in a GMPenvironment.

DETAILED DESCRIPTION

Before the present disclosure is described in greater detail, it is tobe understood that this disclosure is not limited to particularembodiments described, as such may, of course, vary. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting, since the scope of the present disclosure will be limited onlyby the appended claims.

Where a range of values is provided, it is understood that eachintervening value, to the tenth of the unit of the lower limit (unlessthe context clearly dictates otherwise), between the upper and lowerlimit of that range, and any other stated or intervening value in thatstated range, is encompassed within the disclosure. The upper and lowerlimits of these smaller ranges may independently be included in thesmaller ranges and are also encompassed within the disclosure, subjectto any specifically excluded limit in the stated range. Where the statedrange includes one or both of the limits, ranges excluding either orboth of those included limits are also included in the disclosure.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this disclosure belongs. Although any methods andmaterials similar or equivalent to those described herein can also beused in the practice or testing of the present disclosure, the preferredmethods and materials are now described.

As will be apparent to those of skill in the art upon reading thisdisclosure, each of the individual embodiments described and illustratedherein has discrete components and features which may be readilyseparated from or combined with the features of any of the other severalembodiments without departing from the scope or spirit of the presentdisclosure. Any recited method can be carried out in the order of eventsrecited or in any other order that is logically possible.

Embodiments of the present disclosure will employ, unless otherwiseindicated, techniques of biochemistry, biology, flow dynamics,analytical chemistry, and the like, which are within the skill of theart. Such techniques are explained fully in the literature.

The following examples are put forth so as to provide those of ordinaryskill in the art with a complete disclosure and description of how toperform the methods and use the compositions and compounds disclosed andclaimed herein. Efforts have been made to ensure accuracy with respectto numbers (e.g., amounts, temperature, etc.), but some errors anddeviations should be accounted for. Unless indicated otherwise, partsare parts by weight, temperature is in ° C., and pressure is inatmosphere. Standard temperature and pressure are defined as 25° C. and1 atmosphere.

Before the embodiments of the present disclosure are described indetail, it is to be understood that, unless otherwise indicated, thepresent disclosure is not limited to particular materials, reagents,reaction materials, manufacturing processes, or the like, as such canvary. It is also to be understood that the terminology used herein isfor purposes of describing particular embodiments only, and is notintended to be limiting. It is also possible in the present disclosurethat steps can be executed in different sequence where this is logicallypossible.

It must be noted that, as used in the specification and the appendedclaims, the singular forms “a,” “an,” and “the” include plural referentsunless the context clearly dictates otherwise. Thus, for example,reference to “a support” includes a plurality of supports. In thisspecification and in the claims that follow, reference will be made to anumber of terms that shall be defined to have the following meaningsunless a contrary intention is apparent.

Discussion

Embodiments of the present disclosure provide for analysis systems thatare configured to extract a fluid sample from a fluid (e.g., aqueoussolution) in a reactor (e.g., bioreactor) at a first rate and then flowthe fluid sample to a sensor system at a second rate to analyze thefluid sample. The sensor system can detect the presence and/orconcentration of molecules (e.g., biomolecules such as biomarkers (e.g.,metabolites, proteins, peptides, cytokines, growth factors, DNA, RNA,lipids) and cells of different types and cell properties, e.g.,mechanical stiffness, etc.)). The data obtained can be used by afeedback control system to modify, as needed, the conditions in thereactor to enhance the productively of the reactor. The first rate andthe second rate can be different to accommodate different conditions andrequirements of various systems of the analysis system so that theanalysis system can operate in real-time to enhance productivity of thereactor. The analysis system can include aseparation/fractionation/trapping system and/or a mass exchanger toalter the components present in the fluid sample to be analyzed so thatbeneficial information about the fluid in the reactor can be obtainedand used in the feedback control system.

The fluid samples can be obtained using a non-invasive, sterile, andhighly localized sampling system so that inline and real-time analysisof the fluid sample can be achieved, which also allows for adjustmentsto the reactor to be made in real-time using the feedback controlsystem. The analysis system is tunable in regard to fluid sample volume,rates of capture, and rates of flow within the system to enabletransient analysis of complex chemical or biological systems in thereactor fluid. The analysis system is configured to provide highly timeresolved pressure differential pumping for specific volume sampling athighly controllable flow rates, which are tunable for different timeframes and a wide range of flow rates in different regions of theanalysis system. The flow system of the analysis system can decouple theflow rate for extraction and flow rate into the sensor system, which isadvantageous since a wide range of conditions for different systems.

When the reactor is a bioreactor, the analysis system is capable oncontinuous measurement of components in the fluid (e.g., biomolecules,intact cells, microcarriers including intact cells) in multiplelocalized regions. The fluid sample can be obtained at a rate suitableto obtain the desired concentration of the components and then, ifneeded, separate unwanted components (e.g., particles, debris, and thelike) and condition the fluid sample to enhance the analytical detectionof the components. The fluid sample can then be flowed to the sensorsystem at an appropriate rate for the particular analytical device.Results from the analysis can then be used by the feedback controlsystem to adjust, as needed, the conditions in the bioreactor so thatthe desired results are achieved. This process can be repeated in aniterative manner over any desired time period.

Having described embodiments of the analysis system generally,additional details are now provided. The analysis system can include aflow system, a sampling system, a reactor, a sensor system, and afeedback control system. The analysis system can also optionally includean n-stage separation/fractionation/trapping system (also referred to as“separation system”) and/or a mass exchanger. Components such as tubingand valves can interconnect the various systems, the reactor, and themass exchanger to transport fluid samples throughout the analysissystem. Reference is often made to the various systems, the reactor, andthe mass exchanger being in “fluidic communication”, which means thatcomponents such as tubing and valves known in the art can connect thevarious systems, the reactor, and the mass exchanger. While there ismuch detail regarding the interconnection of the various systems, thereactor, and the mass exchanger, not every conceivable variable isprovided but one of skill would understand how to design and constructthe interconnections.

In an aspect, the analysis system can be configured in a number ofdifferent ways some of which are shown and described in reference toFIGS. 1.1, 1.2A, 1.2B, and 1.3 below. In general, the flow system is influidic communication with the sampling system. The sampling system isin fluidic communication with the reactor. In this way, a fluid samplecan be extracted from a fluid in the reactor and flowed at a first rateto the flow system. Then the fluid sample can be flowed at a second ratefrom the flow system to the sensor system. The fluid sample can beanalyzed using the sensor system. The sensor system can include multipletypes of analytical devices to analyze the fluid sample. In an aspect,multiple analytical devices can analyze the fluid sample in parallel orserially to generate information and data. The information and dataproduced by the sensor system can be provided to the feedback controlsystem. The feedback control system can, if necessary based on theinformation and data, adjust one or more parameter or variables withinthe reactor to enhance or otherwise alter the conditions (e.g., pH,temperature, concentration of one or more components, introduce newcomponents, and the like) in the reactor to produce desirable results.The analysis system can be operated in a continuous manner so that thereaction is operating at a desired performance level.

In another aspect, that system can include the n-stageseparation/fractionation/trapping system. In one configuration, then-stage separation/fractionation/trapping system can be in fluidiccommunication with the sampling system and the flow system so that thefluid sample flows from the sampling system to the n-stageseparation/fractionation system and then to the flow system. In anotherconfiguration, the n-stage separation/fractionation/trapping system canbe in fluidic communication with the flow system and the sensor systemso that the fluid sample flows from the flow system to the n-stageseparation/fractionation/trapping system and then to the sensor system.In any configuration, the system can be configured so that the fluidsample can be flowed through the n-stageseparation/fractionation/trapping system multiple times prior toproceeding further in the system.

In addition, the system can include the mass exchanger. In oneconfiguration, the mass exchanger can be in fluidic communication withthe sampling system and the flow system so that the fluid sample flowsfrom the sampling system to the mass exchanger and then to the flowsystem. In another configuration, the mass exchanger can be in fluidiccommunication with the flow system and the sensor system so that thefluid sample flows from the flow system to the mass exchanger and thento the sensor system. In any configuration, the system can be configuredso that the fluid sample can be flowed through the mass exchangermultiple times prior to proceeding further in the system.

In another embodiment, the system can include the n-stageseparation/fractionation/trapping system and the mass exchanger. In oneconfiguration, the n-stage separation/fractionation/trapping system canbe in fluidic communication with the sampling system and the massexchanger, while the mass exchanger is also in fluidic communicationwith the flow system. The fluid sample flows from the sampling system tothe n-stage separation/fractionation/trapping system, to the massexchanger, and then to the flow system. In another configuration, then-stage separation/fractionation/trapping system can be in fluidiccommunication with the flow system and the mass exchanger, while themass exchanger is also in fluidic communication with the sensor system.The fluid sample flows from the flow system to the n-stageseparation/fractionation/trapping system, to the mass exchanger, andthen to the sensor system. In a further configuration, the n-stageseparation/fractionation/trapping system can be in fluidic communicationwith the sampling system and the flow system, while the mass exchangeris in fluidic communication with the flow system and the sensor system.The fluid sample flows from the sampling system, to the n-stageseparation/fractionation/trapping system, to the flow system, to themass exchanger, and then to the sensor system. In any configuration, thesystem can be configured so that the fluid sample can be flowed throughthe n-stage separation/fractionation/trapping system and/or the massexchanger multiple times prior to proceeding further in the system. Inthis and other configurations provided herein, multiple passes throughthe n-stage separation system and/or mass exchanger can be performed toimprove the sample treatment(separation/fractionation/purification/conditioning) for improvedperformance (e.g., sensitivity, resolution, and selectivity of detectionby the sensor system).

The fluid sample will be defined differently if it is flowed throughone, both, or neither of the n-stage separation/fractionation/trappingsystem and/or mass exchanger, but the “fluid sample” will be analyzed bythe sensor system regardless. For example, where the stageseparation/fractionation/trapping system and mass exchanger are notpresent, the fluid sample is referred to as the “fluid sample.” Whenonly the n-stage separation/fractionation/trapping system is present inthe analysis system, the fluid sample is referred to as the“separation/fractionation/trapping fluid sample” or the “separationfluid sample.” When only the mass exchanger is present in the analysissystem, the fluid sample is referred to as the “conditioned fluidsample.” When both the n-stage separation/fractionation/trapping systemand the mass exchanger are present in the system, the fluid sample afterpassing through the n-stage separation/fractionation/trapping system isreferred to as noted above but then is referred to differently (e.g.,conditioned fluid sample) after it passes through the mass exchanger.Thus, even though reference will be made to “fluid sample” generally inmany instances, reference to “fluid sample” is understood to include afluid sample flowed through one, both, or neither of the n-stageseparation/fractionation/trapping system and/or mass exchanger.

FIGS. 1.1, 1.2A, and 1.2B illustrate various configurationschematically. FIG. 1.1 illustrates an embodiment of the analysis system10 of the present disclosure. The analysis system 10 can include a flowsystem 12, a sampling system 30, a reactor 40, a sensor system 50, and afeedback control system 60. The flow system includes a pump system 14and a valve system 16. The pump system 14 and the valve system 16 can betwo distinct components or a single component. The flow system 12 (e.g.,the pump system 14 including a syringe pump) can be used to flow a fluidsample (e.g., which may contain components or chemicals of interest)from the fluid in the reactor 30. The pump system 14 can draw (cause thefluid to flow) the fluid sample via valve system 16 and sampling system30. The sampling system 30 can include one or more sampling structuresthat can be inserted or placed adjacent or within the fluid in any x-,y-, and z-direction, such that the fluid samples from different samplingstructures can interact (e.g., mix) or flow separately. The samplingsystem 30 can also be stationary and the reactor or the structureinclude the fluid is moved in the x-, y-, and z-direction to obtainedthe fluid sample form the desired three dimensional location in thefluid. The sampling structure can have dimensions and/or structures sothat certain sizes of components or chemicals can be flowed into thesampling system 30. The valve system 16 can include one or more valves(e.g., two way valve) to direct the flow of the fluid sample.

Once the fluid sample is flowed to the flow system 12, the fluid samplemay be stored (e.g., temporarily) or immediately directed to the sensorsystem 50. In an aspect, the fluid sample is flowed to the pump system14 at a first flow rate and then flowed to the sensor system 50 at asecond flow rate by the switching of the flow path using the valvesystem 16 (e.g., using a two way valve). The first flow rate and thesecond flow rate can be the same or different. In some instances thefirst flow rate may be much slower than the second flow rate based onthe fluid (e.g., component(s) in the fluid, concentration of thecomponent(s), conditions in the reactor, and the like). The second flowrate can be determined based on the configuration of the sensor system50 and the analytical method of analyzing the fluid sample. Once thefluid sample is analyzed, the data can processed and used by thefeedback control system 60 to adjust variables (e.g., add components orreactants, adjust pH, adjust temperature, and the like) in the reactor40 to enhance the performance of the reactor 40.

FIGS. 1.2A and 1.2B illustrate embodiments of the analysis system 10Aand 10B respectively that include an n-stageseparation/fractionation/trapping system 70 and/or mass exchanger 80.FIG. 1.2A illustrates the placement of the n-stageseparation/fractionation/trapping system 70 and/or mass exchanger 80between the sampling system 30 and the flow system 12, while FIG. 1.2Billustrates the placement of the n-stageseparation/fractionation/trapping system 70 and/or mass exchanger 80between the flow system 12 and the sensor system 50. In an embodimentnot illustrated, the n-stage separation/fractionation/trapping system 70can be positioned between the sampling system 30 and the flow system 12and the mass exchanger 80 can be positioned between the flow system 12and the sensor system 50. In regard to the fluid sample, the sensorsystem 50 analyzes the fluid sample, whether that flow through one,both, or neither of the n-stage separation/fractionation/trapping system70 and/or mass exchanger 80.

In regard to FIG. 1.2A, three configurations are provided and each willbe discussed in terms of flow of the fluid sample. In the firstconfiguration, the analysis system 10A includes only the n-stageseparation/fractionation/trapping system 70. From the sampling system30, the fluid sample flows into the n-stageseparation/fractionation/trapping system 70 (a) and then to the flowsystem 12 (b). In the second configuration, the analysis system 10Aincludes only the mass exchanger 80. From the sampling system 30, thefluid sample flows into the mass exchanger 80 (d) and then to the flowsystem 12 (e). In the third configuration, the analysis system 10Aincludes both the n-stage separation/fractionation/trapping system 70and the mass exchanger 80. From the sampling system 30, the fluid sampleflows to the n-stage separation/fractionation/trapping system 70 (a), tothe mass exchanger 80 (c), and then to the flow system 12 (e). Inanother configuration, the fluid sample can be flowed through then-stage separation/fractionation/trapping system 70 multiple timesflowing (x) flow path. In yet another configuration, the fluid samplecan be flowed through the mass exchanger 80 multiple times flowing (y)flow path.

In regard to FIG. 1.2B, four configurations are provided and each willbe discussed in terms of flow of the fluid sample. In the firstconfiguration, the analysis system 10B includes only the n-stageseparation/fractionation/trapping system 70. From the flow system 12,the fluid sample flows into the n-stageseparation/fractionation/trapping system 70 (aa) and then to the sensorsystem 50 (bb). In the second configuration, the analysis system 10Bincludes only the mass exchanger 80. From the flow system 12, the fluidsample flows into the mass exchanger 80 (dd) and then to the sensorsystem 50 (ee). In the third configuration, the analysis system 10Bincludes both the n-stage separation/fractionation/trapping system 70and the mass exchanger 80. From the flow system 12, the fluid sampleflows to the n-stage separation/fractionation/trapping system 70 (aa),to the mass exchanger 80 (cc), and then to the sensor system 50 (ee). Inthe fourth configuration, the analysis system 10B includes both then-stage separation/fractionation/trapping system 70 and the massexchanger 80. From the sampling system 30, the fluid sample flows to then-stage separation/fractionation/trapping system 70 (ff), to the flowsystem 12 (ff), to the mass exchanger 80 (dd), and then to the sensorsystem 50 (ee). In another configuration, the fluid sample can be flowedthrough the n-stage separation/fractionation/trapping system 70 multipletimes flowing (xx) flow path. In yet another configuration, the fluidsample can be flowed through the mass exchanger 80 multiple timesflowing (yy) flow path.

The reactor can be a chemical reactor or bioreactor. The reactorincludes a fluid that includes one or more components (e.g., chemicals,biochemical, cells, microcarriers, and the like). The reactor includesat least one chamber in which the fluid is present. The reactor may alsoinclude other chambers for storing reactants and other agents that canbe used in the fluid. The reactor may also include devices for flowingthe reactants and other agents in the reactor as well as devices forremoving reactants and other agents or other bi-products. The reactorcan include pH and temperature measuring devices to monitor and controlthe conditions in the fluid and outside of the fluid. The reactor can bea bioreactor that can be used to grow cells, where the cells or productsproduced from the cells can be used in technologies such astherapeutics. An embodiment using a bioreactor is discussed in Examples1 and 2. Examples of systems to which the platform is applicable span arange of industries. Some possible industrial applications includepulp/paper, food/agricultural processing, fossil fuel/plasticsproduction, perfume/personal hygiene, pharmaceuticals, etc. whereaqueous samples can be analyzed to provide insight to processingparameters and final product quality. A subset of potential applicationsin the biological space that the system can provide insight to includeraw materials analysis (e.g. cell culture media, donor material,chemicals), 2D and 3D cell cultures for therapeutic cell manufacturingor research applications, antibody/antigen manufacturing (e.g. CHOcells), biologics manufacturing, tissue engineering (e.g. tissueengineered medical products or TEMPs), medical diagnostics (e.g.blood/urine/saliva analysis), etc. The platform may also providediagnostics for environmental monitoring, drug testing, andcontamination detection.

The reactor can be used in chemical, biochemical, or biologicalapplications. In a particular embodiment, the reactor is a bioreactorand the fluid can include an aqueous solution of biomolecules, intactcells, microcarriers including intact cells, and a combination thereof.The biomolecules can include one or more of secretome, metabolome,transcriptome, genome, lipidome, as well as component found in cytoplasmor components found upon lysing a cell. In particular, the biomoleculescan include proteins, peptides, nucleotides, DNA, RNA, sugars,proteases, growth factors, chemokines, cytokines, adhesion molecules,fatty acids, lipids, amines, co-factors, organic acids, polysaccharides,metabolites, and the like. One or more types of biomolecules (e.g.,metabolites, cytokines, and the like) can be used as quality controlindicators, which can be monitored and used in the feedback loop for thefeedback control system.

The flow system functions to transport (e.g., flow) the fluid samplethroughout the analysis system. The flow system can flow the fluidsample at different rates in different regions of the analysis system.For example, the flow system can cause the fluid sample to flow at adifferent rate from the reactor to the flow system relative to the ratefrom the flow system to the sensor system. The ability to operate atdifferent rates is advantageous in extracting the fluid sample and flowto the flow system at a relatively slower rate as compared to the rateof flow of the fluid sample to the sensor system since a larger amountof low concentration components can be extracted under slower extractionrates. The dimensions of the components in the flow system andcomponents interconnecting the various systems, the reactor, and themass exchanger are such that they can effectively transport fluidsamples of the volume of 1 nL to 100 mL. The system can effectivelytransport fluid samples of a volume, which is determined by the size ofthe pump and tubing used but in general can range from about 1 nL to 100mL.

The flow system can include a pump system and a valve system. The pumpsystem and valve system can be integral to one another or can beseparate components. The pump system can include a syringe pump,piezoelectric pump, peristaltic pump, centrifugal pump, positivedisplacement pump, rotary pump, diaphragm pump, or capillary suctionpump. For example, a KDS Scientific Legato 270 syringe pump can cause afluid sample of about 1 pL to 100 mL to flow through the analysissystem. The syringe size can be varied so the will also vary and can beselected as desired. In an example, the flow system includes a 0.5 μLsyringe is capable of 3.06 pL/min while a 140 mL syringe is capable ofup to 215 mL/hr. Other pump types and sizes can be used, as can beenvisioned by one of skill in the art. The valve system can be multi-wayvalves such as a two way valve. For example, in a first configuration, atwo way valve directs the flow of the fluid sample from the reactor tothe pump system and in a second configuration, the two way valve directsthe flow of the fluid sample to the sensor system. The pump systemand/or the valve system can be operated manually and/or by a computersystem.

FIG. 1.3 illustrates a section of the analysis system 10 that depictsthe flow system 12 and the sensor system 50. The section shown in FIG.1.3 can be applied in each embodiment as shown in FIGS. 1.1, 1.2A, and1.2B. The flow system 12 includes a pump system 14 and valve system 16and the fluid sample is flowed into the valve system 16. From the valvesystem 16, the fluid sample is flowed to the pump system 12 (g). In oneoption, the fluid sample can then flow from the pump system 12 to thevalve system 16 (h) and to the sensor system (i), directly or indirectly(e.g., via the n-stage separation/fractionation system 70 and/or themass exchanger 80). In another option, the fluid sample can flow to thesensor system 50 bypassing the valve system 16 (j), directly orindirectly (e.g., via the n-stage separation/fractionation system 70and/or the mass exchanger 80).

The sampling system is configured to extract the fluid sample from thefluid in the reactor in a controlled manner in regard to position in thex-, y-, and z-dimensions. For example, the sampling system includes acontrol system to position an extraction element is a desired location(e.g., position in the reactor fluid, depth of fluid, and relativeposition to microcarriers and/or intact cells. Various configuration areshown in FIGS. 1.4A-1.4C. The extraction element can be a needle, tube,capillary tube, straw, porous flow structure, or the like. Theextraction element can be dimensionally configured to extract the fluidsample. The extraction element can be dimensionally configured toextract the fluid sample comprising a microcarrier including one or moreintact cells. For example, the inner diameter of the extraction elementcan be about 100 um to 1 mm for microcarriers. In another aspect, theextraction element can be dimensionally configured to extract the fluidsample comprising an intact cell and biomolecules but excludingmicrocarriers. For example, the inner diameter of the extraction elementcan be about 10 μm to 100 μm for an intact cell. In another example, theinner diameter of the extraction element can be about 100 nm to 10 μm sothat biomolecules are extracted but microcarriers and/or intact cellsare excluded. In addition, the extraction element can include a filteror other components to limit what is extracted in the fluid sample.

In another aspect, the sampling system could be designed to immobilize(e.g., have an initial diameter that allows in an object and then thediameter narrows) an analyzed object (e.g., a microcarrier withincorporated cells or an individual cell) at the sampling system orificeor at a point just past the orifice in contact with fluid in the reactorsuch that an extracted fluid sample in contact with an analyzed objectis enriched in target molecules. In one aspect of this configuration, asensor such as capacitive or conductive probe or a mechanical resonatorsor a pressure transducer or an optical sensor can be used to detect whenan analyzed object is immobilized.

FIGS. 1.4A-1.4C illustrate three embodiments in how the sampling system30 extracts the fluid sample from the reactor. FIG. 1.4A illustrates theuse of an extraction element 102 that includes an orifice 104 having adiameter to limit the components present in the fluid sample. Theextraction element 102 is placed into the fluid 110 to extract a fluidsample including biomolecules 116 such as secretome, metabolome, genome,lipidome, or combinations thereof whereas intact cells 114 andmicrocarriers 12 are excluded due to the dimensions of the extractionelement 102. FIG. 1.4B illustrates the use of the extraction element 102that includes the orifice 104 having a diameter to limit the componentspresent in the fluid sample but the extraction element 102 is disposedadjacent a microcarrier or an intact cell. As a result, the extractionelement 102 can extract a fluid sample including biomolecules 116 suchas secretome, metabolome, genome, lipidome, or combinations thereofadjacent intact cells 114 and microcarriers 112, while the intact cells114 and microcarriers 112 are excluded due to the dimensions of theextraction element 102. FIG. 1.4C illustrates the use of an extractionelement 102 a that includes a larger diameter extraction element 102 ato extract microcarriers 112 and intact cells 114. As a result, theextraction element 102 a can extract a fluid sample includingbiomolecules 116 such as secretome, metabolome, genome, lipidome, orcombinations thereof and intact cells 114 and microcarriers 112.Although not shown, the diameter of the extraction element can bedesigned to extract intact cells and biomolecules while excludingmicrocarriers.

The sensor system can include an analytical device that can be used toanalyze the fluid sample. The sensor system can include multipleanalytical devices and appropriate interfaces to accept the fluid sampleso that it can be analyzed. It may be desirable to analyze a fluidsample using two or more analytical devices, in which case the fluidsample can be divided between or among the analytical devices andanalyzed. The sensor system can include one or more analytical devicessuch as: ESI-MS, Raman Spectrometer, FTIR Spectrometer, UV-VISSpectrometer, ESEM/SEM, Optical Microscope, Fluorescence Microscope,NMR, Electrochemical Redox and/or Impedance Sensor, Flow Cytometer, andAcoustic Transducer. An embodiment using the ESI-MS is discussed inExamples 1 and 2.

The feedback control system is in communication with the sensor systemand the reactor system (and optionally the pump system) so thatelectronic information (e.g., data, commands, and the like) can becommunicated back and forth. The feedback control system is configuredto use the output (e.g., data, information, and the like) of the sensorsystem to modify conditions of the reactor. The output can be analyzed(e.g., using a computer system) by the sensor system and/or the feedbackcontrol system. The sensor system can analyze the components (e.g.,biomolecules) present or absent in the fluid sample as well as theconcentration of the components. One or more of the biomolecules can bea quality control indicator, which might be of particular interest asthe quality control indicator(s) may provide significant insight intothe conditions, reactions, processes, and the like present in the fluidat any point in time. The analysis system is advantageous in that thecomponents such as quality control indicator biomolecules can beanalyzed as a function of time with minimal disturbance to the reactorand the conditions, reactions, processes, and the like can be adjustedin real-time to enhance the performance of the reactor.

The separation/fractionation/trapping system can be configured toseparate a first group of components from the fluid sample to produce aseparated/fractionated fluid sample. The first group of components caninclude the biomolecules, intact cells, and microcarriers. Componentsthat are separated from the first group of components can include debrisfrom reactions, cells, microcarriers and particles such as precipitatedparticles, vesicles, exosomes, organelles, cell nuclei, and combinationsthereof. The separation/fractionation/trapping system can include one ormore stages, where each stage can include the same or differentseparation or fractionation devices. Each stage can target separatingthe same component or one or more stages can separate differentcomponents in an iterative manner. The separation or fractionationdevice can include size selection filtration or fractionation device,adsorption/adsorption device, dialysis or reverse dialysis device,partition chromatography device, electrophoresis device,floatation/sedimentation device, fluid trapping device, centrifugationdevices, or the like.

The mass exchanger can be configured to condition the fluid sample(e.g., separated/fractionated fluid sample) to produce a conditionedfluid. The conditioning can include removing unwanted components presentin the fluid sample (e.g., remove or reduce the amount of componentsthat can interfere detection of the desired signal such as salts whichcan inhibit mass spectrometry signals), retaining relatively largercomponents such as biomolecules as compared to smaller components (e.g.,small organic molecules), and introducing signal enhancing components,which may lower the detection limit, improve signal to noise ratio,shift charge distributions to mitigate effects of unwanted components,and the like. The conditioning fluid can be aqueous. In regard to massspectrometry, the conditioning fluid may include organic acids such asacetic acid, trifluoroacetic acid (TFA), formic acid, etc (e.g., 0% to100%), which can aid in protonation of biomolecules, for example. Theconditioning fluid can also include one or more of the following:ammonium acetate (e.g., 0% to 100%), m-NBA (e.g., 0% to 100%), propylenecarbonate (e.g., 0% to 100%), ethylene carbonate (e.g., 0% to 100%),sulofane (e.g., 0% to 100%), and organic solvents such as methanol,acetonitrile, isopropyl alcohol (IPA), chloroform, acetone,N-methyl-2-pyrrolidone (NMP), etc. (e.g., 0% to 100%), chemicalstandards (i.e. to reduce instrument drift and enable quantitativeanalysis), and a combination thereof. The ammonium acetate can increasethe acidity in the electrospray plume, enhance protonation, and/orreduce formation of salt adducts. m-NBA can increase the charge state innon-denaturing fluid samples. Methanol, and other organic solvents, canbe used to selectively remove biomolecules with preferential solubilityin the organic solvent and also to shift the charge state distributionof larger biomolecules like proteins through denaturing and unfoldingeffects on the molecule. In the case of inline HPLC or direct ESI-MSanalysis, chemical standards can be added to the conditioning flow tohelp with quantification of sample concentration and accurate massidentification.

Other analytical techniques such as NMR (nuclear magnetic resonance),FTIR (Fourier transform infrared spectroscopy), and SEM (scanningelectron microscope) imaging may require different sample treatments.For instance, for NMR analysis will benefit from the bulk exchange ofwater with deuterium oxide (D₂O) for enhanced detection of moleculesoverlapping in this region of the spectra as well as internal standard(e.g. tetramethylsilane or TMS, 4,4-dimethyl-4-silapentane-1-sulfonicacid or DSS, trimethylsilypropanoic acid or TSP) infusion for exact NMRreference. The same type of exchange will enable FTIR which faceschallenges in highly aqueous solutions. Exchanging less volatile liquidsinto the sample, such as ethylene glycol, will ensure that freezing andor evaporation do not occur during SEM imaging. Other possible sampletreatments and analysis techniques can be envisioned by one of skill inthe art.

The mass exchanger can include two or more flow channels and one or moreselectively permeable membrane, wherein the selectively permeablemembrane are adjacent one or more of the flow channels. In an aspect,the mass exchanger includes a first flow channel having a first flowchannel entrance and a first flow channel exit. In addition, the massexchanger includes a second flow channel having a second flow channelentrance and a second flow channel exit. The first flow channel and thesecond flow channel can be separated from one another by a selectivelypermeable membrane. As fluid flow through the flow channels, the fluidsin each flow channel are in fluidic communication with the selectivelypermeable membrane.

The material defining the first flow channel and the second flow channelcan be made of a polymer, ceramic, glass, silicon, plastic, orpolyamide, metal, or PDMS. Each flow channel can have a height of about1 μm to 1 mm and a width of about 1 μm to 10 mm. The first flow channeland the second flow channel can be adjacent the selectively permeablemembrane for a length of about 100 μm to 100 mm.

The selectively permeable membrane functions to separate unwantedcomponents in the fluid sample from those of interest and/or to causethe introduction of components to the fluid sample to enhancedetectability. The selectively permeable membrane can be made ofmaterial such as aluminum oxide (anodized porous alumina), polymers(e.g. track etch membranes), cellulose, and zeolite, porous metal (e.g.,nanoporous copper), porous graphene and graphene oxide. The selectivelypermeable membrane can be made of or coated with a material that aidsthe formation of conditioned fluid sample. For example, the selectivelypermeable membrane can be made of or coated with a hydrophobicmaterial/hydrophilic material, lipophilic material/lipophobic material,inert material, decorated with selectively (positively or negatively)charged chemical compounds, electrically conducting, semiconducting orinsulating material, and combinations thereof. The selectively permeablemembrane can have a porosity of about 5% to 95%. The selectivelypermeable membrane can have a thickness of about 1 nm to 10 μm, a lengthof about 10 μm to 50 mm, and a width of about 10 μm to 10 mm.

In an aspect, the mass exchanger is configured to flow the fluid sampleflow through the first flow channel from the first flow channel entranceto the first flow channel exit and be in fluid communication with theselectively permeable membrane. In addition, the mass exchanger can beconfigured to flow a conditioning fluid through the second flow channelfrom the second flow channel entrance and the second flow channel exitand be in fluid communication with the selectively permeable membrane,where the sample fluid and the conditioning fluid are in communicationthrough the selectively permeable membrane. Although here as well inother embodiments the flow of the fluid sample and the conditioningfluid are in the same direction, the fluid flow can be change so the twoflow counter to one another or across one another.

In another aspect, the mass exchanger includes a first flow channelhaving a first flow channel entrance and a first flow channel exit, asecond flow channel having a second flow channel entrance and a secondflow channel exit, and a third flow channel having a third flow channelentrance and a third flow channel exit. The first flow channel and thesecond flow channel are separated from one another by a firstselectively permeable membrane, while the third flow channel and thesecond flow channel are separated from one another by a secondselectively permeable membrane. The mass exchanger can be configured toflow the fluid sample through the second channel, while also beingconfigured to flow a first conditioning fluid through the first flowchannel and to flow a second conditioning fluid through the third flowchannel. The flow of the fluid sample relative one or both of the firstconditioning fluid and the second conditioning fluid can be an in-lineflow, a counter flow, or a cross flow with different relative angle oforientation between different sample and conditioning fluid channels.

In another embodiment, the mass exchanger includes the first flowchannel having the first flow channel entrance and the first flowchannel exit. The first flow channel also include a trapping chamber,where the trapping chamber is dimensionally configured to trap one ormore microcarriers or cells. The first flow channel can be configured tolyse the cells of the microcarriers or the cells, where the contents(e.g., biomolecules on the cell, microcarrier, or in the cytoplasm) flowthrough the remainder of the first flow channel and are in fluidiccommunication with the selectively permeable membrane. The lysing can beachieved using devices that can perform electroporation, chemicaldigestion, mechanoporation, sonoporation, and thermoporation, osmoticstressing or a combination thereof. Depending upon the lysing techniqueused, one or more components of the lysing device can be positioned atone or more locations or positioned in the mass exchanger to achievelysing.

In yet another example, the mass exchanger includes a first flow channelhaving a first flow channel entrance and a first flow channel exit, asecond flow channel having a second flow channel entrance and a secondflow channel exit, and a third flow channel having a third flow channelentrance and a third flow channel exit. The first flow channel and thesecond flow channel are separated from one another by a firstselectively permeable membrane, while the third flow channel and thesecond flow channel are separated from one another by a secondselectively permeable membrane. The mass exchanger is configured to flowa first conditioning fluid through the first flow channel and flow asecond conditioning fluid through the third flow channel. The secondflow channel further includes the trapping chamber such as thatdescribed above and herein, where the components generated from lysingflow through the remainder of the first flow channel and are in fluidiccommunication with the first selectively permeable membrane and thesecond selectively permeable membrane.

Having described embodiments in general, additional detail regardingvarious embodiments are now provided. FIG. 1.5A illustrate embodimentsof the mass exchanger 200 a. The mass exchanger 200 a include a firstchannel 202, a second channel 204, and selectively permeable membrane206 disposed between the first channel 202 and the second channel 204.The first channel 202 includes an inlet 202 a and an exit 202 b wherethe fluid sample flows along path (q). The second channel 204 includesan inlet 204 a and an exit 204 b where the fluid sample flows along path(r) (in-line with flow (q)) or along path (s) (counter flow to (q)).

FIG. 1.5B illustrate embodiments of the mass exchanger 200 b. The massexchanger 200 b include a first channel 212, a third channel 215, afirst selectively permeable membrane 216 a disposed between the firstchannel 212 and the third channel 215, a second channel 214, and asecond selectively permeable membrane 216 b disposed between the thirdchannel 215 and the second channel 214. The third channel 215 includesan inlet 215 a and an exit 215 b, where the fluid sample flows alongpath (qq). The first channel 212 includes an inlet 212 a and an exit 212b where the fluid sample flows along path (rr) or (ss), one of which maybe in-line with path (qq) and the other counter flow to (qq). The secondchannel 214 includes an inlet 214 a and an exit 214 b where the fluidsample flows along path (rr′) or (ss′), one of which is in-line withflow (qq)) and the other counter flow to (qq).

FIG. 1.6A illustrate embodiments of the mass exchanger 200 c. The massexchanger 200 c include a first channel 222, a second channel 224, andselectively permeable membrane 226 disposed between the first channel222 and the second channel 224. The first channel 222 includes an inlet222 a and an exit 222 b where the fluid sample flows along path (t). Thefirst channel 222 also includes a trapping chamber 222 c to trap amicrocarrier or intact cell, where the trapping chamber 222 c hasdimensions according to the size of the microcarrier (e.g., diameter ofmicrocarrier of about 25 to 500 μm) or intact cell (e.g., volume ofintact cell of about 0.5 μm to 200 μm) of interest. Although not shown,the first channel 222 can include components to assist in lysing thecells on the microcarrier or the intact cell, where the components areparticular to the lying technique and positioned accordingly within thefirst chamber 222 and/or the trapping cell 222 c. The second channel 204includes an inlet 204 a and an exit 204 b where the fluid sample flowsalong path (r) (in-line with flow (q)) or along path (s) (counter flowto (q)).

FIG. 1.6B illustrate embodiments of the mass exchanger 200 d. The massexchanger 200 d include a first channel 232, a third channel 235, afirst selectively permeable membrane 236 a disposed between the firstchannel 232 and the third channel 235, a second channel 234, and asecond selectively permeable membrane 236 b disposed between the thirdchannel 235 and the second channel 234. The third channel 235 includesan inlet 235 a and an exit 235 b, where the fluid sample flows alongpath (tt). The third channel 235 also includes a trapping chamber 242 totrap a microcarrier or intact cell, where the trapping chamber 242 hasdimensions according to the size of the microcarrier (e.g., diameter ofabout 25 to 500 μm) or intact cell (e.g., volume of about 0.5 μm to 200)of interest. Although not shown, the third channel 235 can includecomponents to assist in lysing the cells on the microcarrier or theintact cell, where the components are particular to the lying techniqueand positioned accordingly within the third channel 235 and/or thetrapping cell 242. The first channel 232 includes an inlet 232 a and anexit 232 b where the fluid sample flows along path (uu) or (vv), one ofwhich may be in-line with path (tt) and the other counter flow to (tt).The second channel 234 includes an inlet 234 a and an exit 234 b wherethe fluid sample flows along path (uu′) or (vv′), one of which isin-line with flow (tt)) and the other counter flow to (tt).

EXAMPLES

Now having described the embodiments of the disclosure, in general, theexamples describe some additional embodiments. While embodiments of thepresent disclosure are described in connection with the example and thecorresponding text and figures, there is no intent to limit embodimentsof the disclosure to these descriptions. On the contrary, the intent isto cover all alternatives, modifications, and equivalents includedwithin the spirit and scope of embodiments of the present disclosure.

Example 1

Emerging cell therapies have been shown to successfully treat a range oflife threatening illnesses and injuries. The technologies enabling celltherapies can be also used to develop new drugs, and can serve as modelsfor in vitro studies, but are currently not widely available.¹⁻⁷ Inorder to enable large scale and cost effective adaption of celltherapies, quality control methodologies and standards for therapeuticcell manufacturing need to be established.^(1, 8-10) During theproduction of therapeutic cells, levels of metabolites,¹¹ cytokines¹²⁻¹⁴and other proteins¹⁵ or biomolecules can be monitored as quality controlindicators (QCIs) directly related to cell health, efficacy, anddifferentiation. Since potential QCIs span a range of molecular weights,are different for different cell types, and are present in widelyvarying concentrations, an effective QCI discovery and quality controltool should enable sensitive, untargeted identification and dynamic(transient, spatially resolved) detection of the diverse biomolecules,regardless of molecule size and abundance within a sterile bioreactorenvironment.

Current online methods for continuous monitoring of therapeutic cellcultures, such as pH measurement, temperature measurement, off-gas massspectrometry, infrared and near infrared spectroscopy, and Ramanspectroscopy noninvasively capture bulk characteristics, but are subparin offering detailed information such as the secretome's completebiochemical composition or spatial heterogeneity within thebioreactor.¹⁶ Some progress has been made in the implementation ofnon-invasive technologies that deliver multi-dimensional informationabout the cell population in a bioreactor. For instance, advanced imageprocessing techniques have been developed that provide the ability tonon-invasively track and monitor cell growth, measuring cell count anddistribution on microcarriers in a bioreactor.¹⁷ Similarly, two-photonmicroscopy using endogenous fluorophores has been demonstrated to becapable of monitoring stem cell differentiation.¹⁸ While these methodsare non-destructive and provide bioreactor status information that helpsmaintain a viable environment for cell growth, they provide only partialinformation important to predicting cell health and efficacy.^(16, 19)As of yet, the level of quantitative detail required for high fidelityQCIs has only seemed obtainable via offline methods such as LC-MS,¹⁵microarrays,²⁰ and enzymatic assays,^(16, 21) which have been the mainworkhorses in characterization of bioreactor processes and biomarkerdiscovery; however, offline methods suffer from significant time delaysand low throughput which limits their utility for online reactormonitoring. Bringing the power of MS analysis to online diagnostics ofthe complex biochemical environment of bioreactors is thus the mainmotivation for this work, which brings together the advances inmicrofabrication and packaging with an innovative approach to rapidsample preparation in microfluidic format to enable dynamic ESI-MSanalysis of biomarkers from the native environment of cell bioreactors.

Electrospray ionization mass-spectrometry (ESI-MS) is an excellentcandidate for biochemical analysis due to its broad molecular weightcoverage and capacity for unlabeled biomolecule detection and discovery.ESI-MS preserves the state of large biomolecules (“soft ionization”)with no fragmentation, requires no a priori labeling of biomolecules,and is very sensitive to low concentrations of chemicals (limits ofdetection in the nanomolar to even picomolar range).²² However, directonline ESI-MS of cell media is plagued by sample preparationchallenges,²³ imposing the requirement on an online sampling approachfor ESI-MS to rapidly treat samples, removing compounds incompatiblewith MS and modifying the sample composition such that ESI-MS analysisis possible. Key requirements for an effective on-line ESI-MS bioreactormonitoring tool include spatially and temporally resolved sampling ofreactor samples. The ideal online monitoring device should have alocalized (˜cell size) sampling domain (i.e., small inlet) to detectspatial heterogeneity, and this inlet should be easily incorporated withmultiple bioreactor types, e.g., roller bottles, stirred suspensions,wave type, rotating wall, parallel plate, fixed and fluidized bed,continuous flow, etc.^(24,25,26). The Dynamic Mass Spectrometry Probe(DMSP) (FIG. 2.1) is a monolithic cell-bioreactor monitoring tool, whichas demonstrated in this work, has the ability to rapidly condition andanalyze representative cell-bioreactor mixtures via online ESI-MS, andis therefore an important contribution to the suite of methods usefulfor cell therapy quality control indicator discovery and monitoring.

Dynamic Mass Spectrometry Probe (DMSP) Design

The DMSP (Dynamic Mass Spectrometry Probe) is a continuous flow,spatially resolved, biochemical sampling platform capable of detectinglow concentration biomolecules in ˜1 minute. The device is comprised ofthree elements (FIG. 2.1): 1) a sampling inlet, which can be easilyinterfaced with most pumps and cell-bioreactors; 2) a microfabricatedmass exchanger which simultaneously removes compounds not amenable to MSanalysis, such as inorganic salts, and infuses compounds from the activeconditioning stream that enhance MS analysis, e.g. acids forprotonation²⁷, and supercharging molecules for enhanced sensitivity²⁸;and, 3) a nano-electrospray ionization emitter for direct infusion tothe mass spectrometer for quantitative/qualitative analysis.

The DMSP mass exchanger is microfabricated and subsequently interfacedwith an inlet capillary made from PEEK with a 360 μm outer diameter anda 150 μm inner diameter (IDEX Health and Science) and an outlet (forESI-MS) of fused silica with a 360 μm OD, 75 μm ID, and a 30 μm taperedoutlet (New Objective, Inc.). The microfabricated mass exchangerconsists of a 200 μm wide×5 μm tall sample channel that runs between theinlet/outlet capillaries. The sides of the sample channel are defined bySU-8 3005, a biocompatible photoresist.²⁹ Above the sample channel is ananoporous alumina membrane which inhibits the diffusion of largerbiomolecules of interest but allows free diffusion of mass spectrometryinterfering species, such as inorganic salts, into the high flow rateactive conditioning channel. The bottom of the sample channel is definedby a 3 μm SiO₂ deposited on top of the silicon base wafer. Themicrofabrication processes (FIG. 2.2) are carried out on 500 μm siliconwafers which provide structural support during fabrication and deviceoperation. DMSP's active sample conditioning allows for the modificationof a biological sample in three ways. 1) By removing unwanted components(labeled “interference removal”, FIG. 2.1) such as salts which inhibitMS analysis; 2) By retaining comparatively large biomolecules (proteins,peptides, metabolites) which are target quality control indicators(QCIs) of cell culture state; and 3) By introducing MS signal enhancingmolecules (labeled “conditioning exchange”, FIG. 2.1), such assupercharging agents, that lower the limit of detection, improve thesignal to noise ratio, and shift the charge state distributions tofurther mitigate the effect of parasitic salt interference in MSanalysis. Using microdialysis, salts are removed from the samplepreferentially to large biomolecules due to disparity between therespective species' diffusion coefficients, and hence the rate ofdiffusion of the different size molecules from the sample channel intothe active sample conditioning channel³⁰ Some of the target biomoleculesare lost during sample treatment due to parasitic cross-over, but with areduction in pore diameter within the membrane separating the sample andactive conditioning channels, the selectivity of DMSP can be furtherenhanced to expand the range of molecular weight of retained targetbiomarkers without sacrificing the effectiveness of the salt removal.³⁰

Examples of Different Modes of DMSP Operation and Effectiveness

DMSP “Active” Sample Conditioning

Active sample conditioning enhancement of ESI-MS analysis was exploredthrough comparison of the effect of four different active conditioningsolutions. The MS spectra in FIG. 2.3 indicate the effect of DMSPtreatment depending on the composition of the conditioning solution. Allfour conditioning channel solutions contain 1% acetic acid (AA) tofacilitate the protonation of target biomolecules²⁷. Three of theconditioning solutions contain additional compounds that have been shownto improve ESI-MS analysis. Ammonium acetate has been reported toincrease the acidity in the electrospray plume, enhancing protonation,³¹and to reduce the formation of salt adducts.^(32, 33) Superchargingmolecules can also improve sensitivity. In particular, m-NBA (inconcentrations from 1-20%) has been shown to increase charge statedistribution in non-denaturing solutions. Although the mechanism is notcompletely settled, it seems that supercharging occurs due to the lowvolatility and low surface tension of the super charging agent, acombination that promotes the Coulombic droplet fissionprocess.^(28, 34) Finally, methanol (MeOH) is a common ESI-MS solventwhich can shift charge state distribution due to a denaturing, orunfolding, of biomolecules, revealing more locations for protonation vs.the molecule in its natural folded state.³⁵

A solution of 100 mM KCl with 5 μM cytochrome-c was used to carefullycharacterize the effect of active sample treatment using the fourconditioning solutions. In order to maintain relevance for cellmanufacturing, the salt level is similar to that expected in abioreactor³⁶, and cytochrome-c is an appropriate model protein in themass range (˜12 kDa) of signaling molecules secreted from cells, such ascytokines, that are expected to be key quality indicators for cellhealth.^(12-14, 23) FIGS. 2.3A-2.3E shows the effect that each of thefour sample treatments has on the resulting mass spectra. Whenuntreated, the spectra obtained for ESI-MS of 100 mM KCl with 5 μM cyt-c(FIG. 2.3A) has no peaks associated with cyt-c, indicating that theadded KCl levels are high enough to form adducts and clusters thatrender MS analysis incapable of identifying the protein—this isindicative of how the environment of a cell bioreactor will maskbiomolecule signals in direct ESI-MS analysis, and motivates the needfor online sample treatment. In the presence of even moderate saltcontent, adduct formation of analyte molecules makes identificationchallenging to impossible, and also reduces signal intensity, as doescharge competition due to cluster formation. Salt concentrations need tobe reduced to below about 1 mM for identification of cytochrome-c at aconcentration of 5 μM, therefore at least 99% of the KCl in thesolutions tested here was removed in order to produce these spectra. Incell monitoring applications, analyte concentrations could be lower than5 μM which is why a highly selective mass exchanger, such as DMSP, isnecessary to remove salt while retaining biomolecules of interest.

Combined removal of salt and introduction of acetic acid for improvedprotonation results in a mass spectra characteristic of uncontaminatedcyt-c (FIG. 2.3B). The most intense peak (m/z˜1374) is due to cyt-cmolecules charged via addition of nine protons (called the +9 chargestate). The appearance of cyt-c spectral peaks in online analysis of ahigh salt solution occurs as a result of several mechanisms. Firstly,the removal of salt reduces adduction of the protein, so that theprotein is charged via protonation. Salt removal also eliminates theformation of cluster peaks, reducing charge competition andcorresponding signal suppression. Secondly, addition of AA into thesample via active sample treatment further increases the rate ofprotonation due to the increased H⁺ concentration.

Adding 40 mM ammonium acetate to the conditioning solution increases theintensity of the largest cyt-c associated peak˜10⁵ (FIG. 2.3B, 1% AAtreatment) to ˜10⁶, with no shift in charge state distribution (FIG.2.3C). Ammonium acetate is often added in HPLC-MS workflows as a buffer,and has been shown to result in acidification of droplets produced viaelectrospray ionization and thus provide additional protons for analytecharging.³¹ While addition of ammonium acetate further enhancesprotonation and mitigates salt adducts,³³ because ammonium acetate doesnot change the surface tension and has little to no denaturing effect onthe protein so there is no apparent impact on the charge state: changesin MS spectra peak intensities are due to enhanced protein protonationwith simultaneous suppression of salt adduction.

In contrast, adding supercharging molecule m-NBA at 2% to theconditioning flow results in a significantly shifted charge statedistribution (FIG. 2.3D). What this means is that the protonated peakwith 15H⁺ attached to the biomolecule is most dominant, at about m/z824. Although not producing the same maximum peak intensity as ammoniumacetate, m-NBA causes a marked increase in the prevalence of spectralpeaks corresponding to the highly charged protein. This suggests thepossibility of using online m-NBA treatment for higher molecular weightproteins that would usually be outside the range of a given massspectrometer. For instance, the Bruker MicroTOF used in theseexperiments has a maximum m/z range of 30-5000. Thus increasing themaximum charge placed on proteins from 10 to 20, for example, wouldraise the size of the largest detectable protein from 50 kDa to 100 kDa.The impact of supercharging is also seen in the potential for improvedsensitivity. This is because tuning the ion transfer voltages in a massspectrometer to acquire signals over the entire available m/z rangesacrifices sensitivity compared to that obtainable when tuned for asmaller m/z range. As seen in FIG. 2.3D, super charging compresses therange of m/z values for peaks for cyt-c.

Addition of 50% methanol (MeOH) also has a significant effect on thecharge state distribution (FIG. 2.3E) although the reason is differentthan in supercharging with m-NBA. This treatment creates a spectra inwhich the +14 charge state is most abundant, which suggests that theeffects of m-NBA and MeOH are not identical. Methanol denatures theprotein, revealing more sites for protons to attach, which results in ashift to higher charge state distributions. Although MeOH, similarly tom-NBA, has a lower surface tension than water, it is more volatile thanwater. Therefore it does not produce the same increase in dropletfission events that m-NBA does.^(28, 35) Due to a lower solubility limitof inorganic salts within methanol,³⁷ continuous flow analysis wasdifficult using MeOH as an active conditioner. As MeOH was exchangedinto the sample, salt precipitates would form. These precipitateseventually clogged the sample channel or the ESI emitter, causing theDMSP to cease operation.

SNR and LOD Improvements via DMSP Treatment

The experimental results depicted in FIGS. 2.3A-2.3E show how onlineDMSP treatment affects the ESI-MS analysis of a high salt contentmixture in a qualitative manner, by comparing the charge statedistributions and signal intensities. In FIGS. 2.4A-2.4C we present theimpact of DMSP operation quantitatively by studying the signal to noiseratio (SNR) and limit of detection (LOD). The comparison of activeconditioning solution on SNR and LOD was performed with two conditioningsolutions of 1% AA alone versus 1% AA with 2% m-NBA for varying cyt-cconcentration (0.25, 0.5, 1.0, 2.5, 5, and 10 μM) in 50 mM KCl solution.These experiments were designed to investigate how superchargingmolecules specifically can enhance detection of proteins at lowconcentrations. FIGS. 2.4A-2.4C shows that the addition of 2% m-NBA to1% AA in the conditioning solution both increases SNR and improves LODfor direct infusion nanoESI-MS through application of DMSP. FIG. 2.4Aillustrations that the addition of m-NBA to the active conditionerincreases SNR across multiple charge states. Here, signal to noise ratiois defined as SNR=I_(cyt-c)/I_(avg), where the I_(cyt-c) is theintensity (%) of an identified cyt-c peak and I_(avg) is the averagedintensity (%) of all peaks within a window+/−0.3 m/z of the identifiedpeak. This definition gives a local SNR value for every protonated cyt-cpeak. An SNR above 2.5 corresponds to a distinguishable, fullyprotonated cyt-c peak. In FIG. 2.4A the highest and fifth highest SNRcharge state are plotted as a function of cyt-c concentration.Supercharging molecule m-NBA is known to shift to higher charge statedistributions, but these results demonstrate that it also increases theSNR for detected molecules across multiple charge states. This willallow for easier identification of low concentration biomolecules incomplex mixtures.

For concentrations below 2.5 μM the addition of m-NBA to the activesample conditioner allows for the successful identification of multiplepeaks associated with cyt-c. At low concentrations, 1% AA treatmentalone does not allow detection of cyt-c, which means that the additionof m-NBA during treatment does more than shift the charge statedistribution; it has a useful application in aiding in detection ofbiomolecules by enhancing protonation, and mitigating the effect ofparasitic adducts formed with salt ions, thus lowering the limit ofdetection with DMSP analysis.^(34, 35, 38) Adding m-NBA to theconditioning solution drastically improves the LOD (i.e., the lowestconcentration for which SNR of a peak associated with fully protontatedcyt-c is greater than 2.5) for cyt-c in 50 mM KCl by an order ofmagnitude, from 2.5 μM to 250 nM. FIGS. 2.4B and 2.4C show a comparisonof spectra resulting from DMSP nanoESI-MS analysis of 1 μM cyt-c in 50mM KCl with 1% AA conditioning solution (FIG. 2.4B) vs. 1% AA and 2%m-NBA (FIG. 2.45C) to highlight the significant effect addition of mNBAhas on limit of detection. With 1% AA alone (FIG. 2.4B), nodistinguishable (i.e. high SNR) protonated cyt-c peaks are visible,while treatment with 1% AA and 2% m-NBA produces five distinct peaksfrom completely protonated species (FIG. 2.4C). The concentration ofcell quality indication (QCI) molecules may be even lower than thoseexplored here. Therefore, the ability to detect low concentrationbiomolecules enabled by DMSP treatment is critical for MS application tocell manufacturing control, since a large range in signaling moleculeconcentrations is expected.

At moderate concentrations, i.e., 2.5 μM, where both conditioningsolution compositions successfully reveal cyt-c, m-NBA enhances SNRacross multiple charge states far above the levels obtained withoutm-NBA. With very high SNR peaks across a range of multiply chargedstates, identification of biomolecules is easier, and applications whichcan benefit from biomolecule structural information are enabled throughthe use of tandem (MS/MS) mass spectrometry that performs best withhighly charged species. Although for both 5.0 and 10.0 μM cyt-cconcentrations, 1% AA treatment produces a very high SNR for the highestintensity peak, m-NBA treatment again creates higher average SNR values.These levels of cyt-c concentration (5, 10 μM) are very high, andapproaching the point where signal saturation was observed. At thesehigher levels of cyt-c abundance, detection of the biomolecules is notdependent on treatment type, muting the impact of active sampleconditioning. However, the ability to detect low concentrationbiomolecules (i.e. <2.5 μM cyt-c) through DMSP's active sampleconditioning with m-NBA is the most important conclusion of theseexperiments. This means that DMSP has a compelling capability fordiscovery and/or detection of ultra-low concentration biomoleculesbecause of active sample treatment via inline introduction of SNRenhancing compounds. This type of detection is usually only possiblewith offline methods like HPLC, rendering DMSP a unique and powerfultool for in operando cell-health monitoring.

Multicomponent Detection Enabled by DMSP

The ability to detect and differentiate between multiple biomoleculessimultaneously will enable untargeted discovery, and is a desiredfeature enabling more robust control of bioreactor environment andutility for a diverse spectrum of cell types. To probe the DMSPcapability of multiple protein detection, we performed experiments witha cell buffer mixture (1×phosphate-buffered saline, PBS) containinginterleukin 6 (IL-6), interleukin 8 (IL-8), and cyt-c using twodifferent “active” sample conditioning strategies previously described(FIGS. 2.5A-2.5C). With molecular weights of ˜8 kDa (IL-8), ˜12 kDa(cyt-C), and ˜21 kDa (IL-6), the mixture of these molecules is arealistic proxy for cytokines, which are known to be indicative of cellhealth,^(13, 14) in a solution that has multiple inorganic compounds(i.e. NaCl, KCl, Na₂HPO₄, and KH₂PO₄) in concentrations representativeof a bioreactor environment.³⁶ These experiments again demonstrate thepower of m-NBA treatment for detecting multiple analytes of interestfrom the complex bioreactor mixtures. Specifically, when 1% AA treatmentwas used alone, only IL-6 was detected (FIG. 2.5B), whereas addition 2%m-NBA to 1% AA in active sample conditioning yielded detection of bothIL-6 and cyt-c (FIG. 2.5C). As expected, the baseline ESI-MS of 5 μMIL-6, IL-8, and cyt-c in 1×PBS without DMSP treatment produces nomeaningful detection of either biomarker (FIG. 2.5A). This is anotherpowerful demonstration of DMSP QCI detection capabilities underrealistic conditions when inorganic compounds, which are abundantlypresent in cell bioreactors, can hinder the standard MS analysis ofanalytes by suppressing the relevant molecular signatures viacompetitive metal cation adduction and salt cluster formation, as wellas raising the background noise in the spectra.

When 1% AA is used as the conditioning solution, removal of inorganiccompounds and acidification of the sample in the DMSP reduces chemicalnoise associated with salt adducts and clusters, and IL-6 can bedetected, although the protonated peaks are accompanied by metal cationadducts (FIG. 2.5B). The highest concentration of salt in PBS is NaCl at137 mM, which is drastically higher than the salt levels explored in theexperiments described prior (100 mM FIG. 2.3A-2.3E, 50 mM FIG.2.4A-2.4C). Still, based on relative quantification experiments (notpresented) it is likely the DMSP is removing over 99% of the saltspresent in order to reveal the signal associated with IL-6. However, theSNR is low and peaks formed via salt adducts are visible at higher m/zvalues along with each protonated peak, indicating opportunities forfurther improvements in mass exchange and resulting sample treatment.

When the conditioning solution contains both 1% AA and 2% m-NBA, highSNR peaks due to fully protonated cyt-C and IL-6 appear (FIG. 2.5C).However, even with this level of conditioning IL-8 is not detected. Asthe smallest of the analytes, at ˜8 kDa, IL-8 may have suffered moreparasitic loss in the DMSP mass exchanger than larger in size cyt-c andIL-6.³⁰ The addition of m-NBA to sample treatment shows how additivescan further enhance the performance of DMSP so that it outperforms 1% AAtreatment significantly. This result further emphasizes that m-NBA isnot only an attractive treatment option for supercharging (i.e. shiftingcharge state distributions towards lower m/z range), but also is a veryuseful additive for reducing chemical noise due to metal cationadduction and salt cluster formation. These initial promising resultsdemonstrate that DMSP, with active sample treatment, has an ability tomonitor multiple biomolecules online in a high salt content mixture,essential for bioreactor monitoring.

DMSP Device Design for Improved ESI-MS Performance

DMSP Design Considerations for Improved Selectivity and Reduction ofResponse Time

Important metrics for DMSP performance are analyte retention, sampleresidence time, salt removal, and active conditioner exchangeeffectiveness. Due to the nature of the separation technique, anymolecule that is smaller than the pore diameter in the size selectivenanoporous membrane will diffuse through and therefore be removed from,or, in the case of active conditioning, introduced into the sample.However, reducing the size of the pores in the membrane can increasemolecular selectivity (i.e. larger biomolecules retained, smallerinorganic salts still removed from sample, FIG. 2.6A) at the cost of anincreased resistance to mass transfer through the membrane (FIG. 2.6B).A moderate reduction in pore size only minimally affects the diffusionof small molecules, such as the inorganic salt molecules that aretargeted for removal or conditioning agents introduced into the samplefor ESI-MS enhancement. However, this reduction in pore size will enablea substantial reduction in the effective diffusion coefficient of largerbiomolecules through the nanopores and thus an improved selectivity ofseparation resulting in higher QCI retention in the sample. FIG. 2.6A,which is based on predictions by Tibavinksy et. al.,³⁰ shows that with areduction in membrane pore size the device will improve retention oflarger biomolecules, such as albumin (˜66 kDa), cytochrome-c (˜12 kDa),and insulin (˜6 kDa), while not affecting the diffusion of smallermolecules, such as salt ions. Pore size tuning can be achieved bychanging the solution used for anodization and reducing the anodizationvoltage during fabrication of the membrane.³⁹ It should be noted thatactive conditioners used in these experiments are relatively smallcompared to the nanopores as well (e.g., m-NBA molecular weight ˜150Da), so their infusion to the sample channel will not be hindered. FIG.2.6B shows that pore size cannot be reduced too far, because althoughthe molecular weight cutoff decreases with pore size, the mass transferresistance increases asymptotically as well, which will negativelyimpact the time response of DMSP by requiring a longer residence timefor sufficient sample conditioning. Thus, an optimum pore size willexist between the two extremes, which will be explored as part of theproposed work.

Residence time within DMSP depends on the volume of the entire system,including the inlet capillary, microfabricated mass exchanger, andoutlet ESI emitter. Currently, the microfabricated device has a volumeof ˜20 nL, and the inlet capillary and ESI emitter have dead volumes of˜700 and ˜70 nL respectively. Changing the design of the mass exchangerto minimize dead volume also affects the mass transfer efficiency,however reducing the diameter of the inlet and outlet capillaries cansignificantly decrease sample residence time with no effect on masstransfer (aside from sample dispersion considerations). Therefore,reducing the inlet/outlet capillaries' volume will have the greatesteffect on DMSP response time. When incorporated with a samplinginterface, the minimization of connecting fluidic paths will again be ofcritical importance compared to further minimization of themicrofabricated DMSP. However, with extremely small capillaryconnections to and from the DMSP mass exchanger higher probability ofclogging and increased flow resistances may lead to membrane failure(membrane rupture at ˜800 kPa³⁰) and decreased device robustness. Thistrade-off will be empirically explored through multiple designvariations around the baseline informed by scaling predictions from thefirst-principle mass transfer and fluid flow analysis.

DMSP Mass Transfer Enhancement

In the preliminary experiments, residual salt content and a parasiticloss of the smallest biomolecule to the conditioner channel resulted inan incomplete identification of all three biomolecules in solution(FIGS. 2.5A-2.5C). With active conditioning, particularly usingsupercharging molecule m-NBA as an active conditioner, the device wasshown to be more effective at identifying low concentration biomoleculesin high salt content solutions, but the presence of salt adducts in 1%AA treatment (FIG. 2.5B) and the identification of only two of the threebiomolecules in multicomponent analysis (FIG. 2.5C) motivate a need toenhance the selective removal of salts and increase the infusion ofsample modifiers from the active sample conditioner.

The total rate of mass transfer (directly related to deviceeffectiveness, i.e. percent of salt removed) within the microfabricatedexchanger can be analyzed from a mass transfer resistance approach (FIG.2.7), considering the resistance associated with the sample channel,membrane, and conditioning channel separately. These three resistancesare in series, and by knowing the concentration of any molecule in thesample channel and the conditioning channel the rate of mass transfercan be calculated (FIG. 2.7).

In the sample channel, an assumption is made that the mass transfer byconvection dominates, since the Peclet number is high enough thatdiffusion along the sample flow path is not significant compared toadvective transport. In the size selective membrane separating thesample channel and active conditioner channel, due to low “cross-over”flow rates through the nanopores (low Peclet number) owing to highhydraulic resistance and smaller cross-membrane pressure difference,only diffusion is considered. In the active sample conditioner channelconvective mass transfer is again the dominating effect. Theseresistances for a similar mass exchanger design and componentdimensions³⁰ are shown to be 1) R_(sample)=1/h_(sample)≈1100 s/m 2)R_(membrane)=δ_(m)/ϕD≈10000 s/m and 3)R_(condition)=1/h_(condition)≈36000 s/m, where h_(sample) andh_(conditioner) are the convective mass transfer coefficients inrespective channel, δ_(m) is the membrane thickness, ϕ is the membraneporosity, and D is the effective diffusion coefficient across themembrane nanopores. The two dominating resistances are in the membraneand conditioner channel, which will be the focus of DMSP design andgeometry optimization in the proposed work.

Residence time, t_(res)=V/{dot over (Q)}, in DMSP is a function of thedead volume (V) and flow rate ({dot over (Q)}). The device effectivenessdepends on the resistance to mass transfer in the device and the sampleresidence time, which is inversely proportional to the flow rate duringsample treatment. With a fixed mass transfer resistance, lower flowrates result in a more complete removal of all species (includinganalytes), while too high of a flow rate through DMSP leads to thepartial retention of parasitic compounds in the sample channel Areduction in mass transfer resistance (assuming a fixed dead volume)allows for a decrease in sample residence time, since compounds areremoved at higher rates, allowing for the device to be operated atincreased flow rates. In FIG. 9 the relationship between mass transferresistance and residence time is shown. Improvements for all threeindividual resistances need to be made to minimize the total resistance,but there are operational tradeoffs that provide constraints on thepractical sizes of the membrane thickness and the sample channelgeometry. That is for a fixed nanopore diameter, the mass transferresistance for membrane can be reduced by thinning the membrane thusminimizing the transport length through the pores. However, thereduction in thickness must be balanced with the membrane structuralintegrity such that it can withstand the pressure differential across itbased on the hydrodynamic conditions in the sample and the conditioningflow channel There is an optimal thickness that ensures the fastesttransport across the membrane while maintaining robust operation overthe range of target flow rates.

In the sample channel, due to a simple rectangular geometry and fullydeveloped flow, the Sherwood number correlation (as a function of theReynolds and Schmidt numbers) can be used to estimate the mass transfercoefficient. The mass transfer coefficient scales with the inverse ofsample channel height. Thus, it is desirable to minimize the channelheight under the constraint of a limit on the maximum pressure developedin the sample channel. This pressure should be low enough to maintainstructural stability of the transfer membrane, and to ensure an abilityto pump fluid through the device. This pressure drop is highly sensitiveto changes in the sample channel height, as it scales with the inverseof the channel hydraulic diameter to the fourth power for a constantvolumetric flow rate. Further consideration constraining the minimumchannel size is a requirement for reduced incidence of clogging. In thecurrent design, the height of the channel is kept at 5 μm chosen basedon ease of fabrication and empirical observations of providingsufficiently low mass transfer resistance while avoiding clogs.

In the conditioner channel, due to a complex geometry and a developingmass transfer boundary layer, computational fluid dynamics (CFD)simulations need to be used to estimate the convective mass transfercoefficient, which is spatially varying along the flow direction. Wepropose to use a simulation guided approach to identify the optimal flowconfiguration (e.g. parallel/counter-flow as in the current design vs.cross-flow or serpentine flow pattern for the conditioning fluid) andgeometry (e.g., height reduction to reduce mass transfer whileincreasing the channel width to accommodate higher flow rates withoutexcessive pressure drop) for the conditioner channel. This will yieldthe lowest, but still practically realizable, mass transfer resistance,resulting in greater salt removal and more effective (greater flux)injection of active compounds into a sample for conditioning. Since thesample conditioning channel is currently the component with the highestmass transfer resistance (FIG. 2.8), significant improvements in DMSPperformance could be expected through first-principle analysis andoptimization of convective mass transfer in the conditioning flow.

Tuning of Chemistry for Active Sample Conditioning

In the preliminary work active sample conditioning composition waslimited to only two chemicals in solution at one time (e.g. acetic acidand m-NBA) using a relatively small set of chemical additives (aceticacid, ammonium acetate, m-NBA, and methanol) in concentrations based onvalues found throughout literature. Chemical compounds which eitherdenature or supercharge biomolecules were shown to be beneficial, butapplying these effects in different combinations, and to mixturescontaining several target biomolecules with different properties(hydrophobic vs hydrophilic) and molecular weights using DMSP's activesample conditioning has yet to be fully explored. Additionally, m-NBAand methanol are of limited use in this system, but for differentreasons. In water, m-NBA was observed to be insoluble at levels above 2%by volume, which puts a limit on the amount of m-NBA that can beintroduced to the sample to enhance ESI-MS by supercharging; otherchemicals that are more soluble in water can transfer faster and ingreater amounts into the sample channel due to an increasedconcentration difference, and hence a greater flux (FIG. 2.7). Thismotivates exploration of higher solubility (and higher diffusivity)compounds endowed with supercharging properties (e.g., propylenecarbonate, ethylene carbonate, sulofane) for improving the performance(SNR and LOD) of DMSP across a broad range of target analytes. Methanolwas also shown to shift charge states by denaturing the proteins(allowing for more protonation sites), but a decreased solubility ofinorganic salts in methanol led to salt precipitation and eventualclogging of the DMSP. Exploration of other organic solvents for samplemodification will elucidate how different denaturing solvents, such asacetonitrile, with higher salt solubility to avoid precipitation andclogging, will impact of DMSP ESI-MS sensitivity. The mass transferanalysis described in the previous section will be used to guide theconcentrations and types of molecules used for these active sampleconditioning studies.

Combining more than two active conditioning compounds for dynamic samplemodification in the microfluidic sample treatment channel will alsoyield novel results, potentially exploiting multiple benefits of eachconditioner, leading to a significant improvement in DMSP performance.These studies will be exploratory in nature, and may or may not yieldthe desired benefits of improved analytical sensitivity (SNR and LOD)while maintaining robust operation. Collectively, a fundamentalunderstanding of mass transfer effects in a microfabricated massexchanger through analysis, simulations, and experimental investigationof different chemistries for sample treatment will yield new insight onoperating modes and design criteria for online ESI-MS analysis fromcomplex chemical environments.

Dynamic Sampling Interface (DSI) for DMSP Integration with Bioreactors

Secreted biomolecules from cells growing within a bioreactor can becorrelated to cell health, propagation, anddifferentiation.^(2, 11-14, 23) As cells secrete, the releasedbiomolecules rapidly become less concentrated as they diffuse away fromthe cell membrane. As a result, static, bulk sampling away from thecells will uptake the target biomolecules at significantly reducedconcentrations due to volumetric dilution and time averaging. Further,the presence of high abundance molecules such as growth factors in cellserum provide an overwhelming background for ESI-MS detection of lowabundance secreted target analytes. Therefore, spatially and temporallyresolved sampling is essential for high sensitivity monitoring of cellsecretomes to enable: 1) the mitigation of spatial dilution and timeaveraging of secreted biomolecules by analyzing the secretome in theimmediate vicinity of cells or cell carriers; 2) the capability forcapturing the transient cell secretion events since online sampletreatment by DMSP affords a nearly real-time ESI-MS analysis; and 3) theprobing of spatial heterogeneity of secretomes within the bioreactorenvironment in correlation with the spatial distribution of 2D and 3Dcell cultures.

The chemically complex environment within a cell-bioreactor is notamenable to direct ESI-MS analysis without sample preparation. Vitamins,electrolytes, glucose and other high concentration biomolecules, such asgrowth factors, found in serum contribute to the degradation of MSsignal due to charge scavenging, chemical noise, and convolution of MSspectral features, thus making molecular identification andquantification of low abundance signaling molecules (cytokines,metabolites, etc.) indicative of cell health challenging. Salts andother small dissociated molecules create adducts (proteins charged viametal cation addition) during the electrospray process, which suppressthe signal of fully protonated ions (i.e. charged via protons) andcomplicate the charge state distribution in the MS spectra, which iscritical for molecular identification. As described above, DMSP showedan ability to detect multiple biomolecules, in the mass range similar toexpected signaling molecules, within chemically complex, high saltcontent mixtures via active sample conditioning for online ESI-MSanalysis. Incorporation of a localized sampling interface coupled toDMSP will create a tool capable of monitoring, transiently, thesecretion of low concentration biomolecules to understand how thebiomolecular composition changes throughout the bioreactor volume, whichcan be correlated with functional assays for cell viability and potency.FIGS. 2.9A-2.9B depict the key components of the Dynamic SamplingInterface (DSI) for integration of DMSP with a cell-bioreactor. Theseinclude 1) a pump for bi-directional sample uptake/infusion (e.g.syringe pump), 2) a switching valve for control of the flow directionduring uptake and infusion, 3) a flexible capillary terminated with asmall size orifice to capture small volumes of the reactor contentlocally, e.g. near cells for probing the secretomes; and 4) capillaryconnection to DMSP and MS for online analysis. During sample uptake, theflow rate can be tuned to match the rate and duration of the chemicalsecretion of the cells, thus capturing transient events in the reactor.On the other hand, during infusion, the flow rate should be tuned formatching an optimal flow rate required by DMSP for desalting and activesample conditioning and subsequent ESI-MS analysis, which requires acertain flow rate to maintain stable nanoESI for a given spray capillarydimensions. In FIG. 10 the difference in pressure between ports A and Billustrates how in sample uptake (FIG. 2.9A) the flow can dynamicallychange to capture the secretion dynamics using small extracted liquidvolumes, whereas during infusion (FIG. 2.9B) the pumping pressuredifference is constant for sustaining an optimal, steady flow rateduring DMSP infusion to ESI-MS.

The implementation of DSI is not only a novel technological development,but also a scientifically interesting problem with fluid dynamics andmass transfer considerations playing a critical role in design. Thesampling interface should be able to uptake volumes as small asnecessary to capture the molecular release during the localized cellsecretion event, which is expected to be in the nanoliter range.However, the dead volume of the DMSP is well above this level, not evencounting for tubing and valve dead volumes. Thus, if sampling isperformed into an empty system, the sampled volume would have to be atleast as large as the dead volume to avoid the introduction of airbubbles (which would interfere with DMSP functionality). Therefore theentire sampling interface/DMSP dead volume should be initially filledwith a non-interfering liquid prior to sampling from the bioreactor toenable only a small volume sample is extracted, without the introductionof air bubbles into the system.

As a small sample is taken up into the system, a liquid “plug” will flowthrough the piping and valves of the sampling interface (FIG. 2.10A).Since the liquids are expected to be miscible, dispersion of the sampledliquid plug will dilute the sample as it advances through the samplinginterface and to the outlet/inlet to DMSP. According to Taylor'sdispersion theory,^(40, 41) lower flow rates and smaller capillaryvolumes will result in reduced dispersion effects, but since velocity,transit time, and volume are related to dispersion phenomenon, theoptimal design requires careful optimization, balancing the reduction inthe device response time with improved sensitivity that comes with lessdispersed sample. Another consideration is that as flow rates becomelower, axial diffusion will become significant as Peclet numberapproaches unity. Application of mass transfer theory will assess thesetrade-offs quantitatively, which can be validated using benchmarkelution experiments (via optical or MS sensing). This approach willenable an optimal design and operation of the dynamic sampling interface(DSI) in conjunction with DMSP and MS, yielding the highest sensitivityand fastest response for bioreactor monitoring (FIG. 2.10B). Sampledilution depends on flow rate (Q), diffusion coefficient (D), volume(V). Dispersion theory (Taylor, 1953) governs high Peclet number flow:

${D_{eff} = {D_{AB}\left( {1 + \frac{Pe^{2}}{48}} \right)}}{C_{mean}{\alpha \left( \frac{V}{\overset{.}{Q}} \right)}D_{eff}}$

Online Sampling from Cell Bioreactor

During the engineering optimization of the sampling interface (DSI) andthe DMSP mass exchanger for high sensitivity and high spatial/temporalresolution, DMSP is concurrently characterized for the monitoring ofbioreactor environments. Due to the untargeted approach of ESI-MS, DMSPanalysis of a bioreactor has the unique potential to reveal the localbiochemistry of cell growth and help identify the biomarkers that can becorrelated with the cell culture state under different growthconditions. Many of these findings are difficult to anticipate at thisstage, as the current level of understanding of bioreactor environmentsis limited to offline assays (e.g., Luminex) of bulk samples extractedat a few time points over the course of long (1-2 weeks) cell growthperiods. By sampling at multiple locations throughout the bioreactorvolume, it is expected that spatial heterogeneities in biochemicalcomposition can be uncovered using DMSP, along with identification ofpreviously unknown dynamic changes in the cell secretome.

The ability for DMSP to sample, treat, and analyze via ESI-MS rapidlywill provide insight to the timescales of cell-bioreactor events. DMSPwill elucidate on what time scale biochemicals vary within the volume,and therefore establish minimum analysis time scale for adequatemonitoring of dynamic cell behavior. For instance, if biochemicalcomposition varies at rates much slower than expected, a new design ofthe DMSP sampling interface and mass exchanger can tune future onlinemeasurement for different modes of analysis, e.g. optimizing the systemfor longer sample retention (and hence reduced transient sensitivity)with increased sample selectivity, as well as introducing additionalanalyte pre-concentration and/or size-selective filtering steps tobetter monitor these bioreactors.

DMSP analysis can also probe how different operating conditions affectthe biochemical composition in the bioreactor, thus enabling thehypothesis generation on cell growth behavior mapped onto its secretomeevolution. One question of significant interest and significantpractical importance is how serum free media affects growing cells.DMSP's capability to monitor the cell secretome locally in response to achange in serum may provide important clues relevant to serum utility intherapeutic cell manufacturing. Other factors that might affect themonitored molecules in a bioreactor include the physical design (e.gspinning flask vs. rocking plate), cell type, microcarrierconcentration, media agitation rate, etc., which would providecompelling avenues for future studies once the DMSP and the DSI arefully developed in the course of this dissertation research. The abilityfor DMSP to easily probe a variety of operating conditions intherapeutic cell manufacturing is a high impact outcome of this work.

FIG. 2.11 shows DMSP application to three cell types grown in mediacontaining FBS. Each sample was gathered at 40 uL/hr and infused throughDMSP at 26 uL/hr with active sample treatment using 1% m-NBA 1% AA. Asshown in previous results, this mixture enabled significant improvementsin limit of detection, and also the ability to detect multiplebiomolecules simultaneously. Importantly, these results show that DMSPis capable of sampling a very small volume (2 uL) and detectingdifferences between three cell types.

FIG. 2.12 shows the capability of DMSP to exploit localized sampling todetect low concentration biomarkers secreted by cells. In cell cultures,secreted molecules are in highest concentration near where the cells aregrowing (i.e. the cell membrane), and therefore standard methods such asHPLC and enzymatic assays capture bulk samples of secreted molecules inlow concentration. By sampling near the cells, the local concentrationis drastically increased. The four spectra represent signal gatheredfrom the same cell culture of human fibroblasts cells, which is capturedby direct ESI-MS analysis through DMSP. The first the third spectralabeled “near” are spectra resulting from 2 uL of media extracted fromnear the cells and treated with 1% AA 1% m-NBA via active sampleconditioning in DMSP. The second and fourth spectra, labeled “far” arefrom 2 uL sampled in the bulk media, again treated with 1% AA 1% m-NBA.The difference between localized and bulk sampling demonstrates thepowerful effect that DMSP will have on detection of important biomarkersin cell cultures. The results in FIGS. 2.11 and 2.12 show that DMSP hasalready demonstrated the ability to monitor differences betweendifferent cell types, as well as the ability to rapidly and repeatedlysample from cell cultures to detect biomolecules. The enhanced spatialand temporal resolution provided by DMSP will provide new discovery andquality control capabilities in cell manufacturing.

EXAMPLE 1 REFERENCES

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Sakiyama-Elbert Shelly,    Stem cells for spinal cord injury: Strategies to inform    differentiation and transplantation. Biotechnology and    Bioengineering, 2016. 114(2): p. 245-259.-   8. De Sousa, P. A., et al., Development and production of good    manufacturing practice grade human embryonic stem cell lines as    source material for clinical application. Stem Cell Research, 2016.    17(2): p. 379-390.-   9. Lipsitz, Y. Y., N. E. Timmins, and P. W. Zandstra, Quality cell    therapy manufacturing by design. Nature Biotechnology, 2016. 34: p.    393.-   10. du Moulin Gary, C., et al., A 3-year experience of quality    control and quality assurance in the multisite delivery of a    lymphocyte-based cellular therapy for renal cell carcinoma.    Biotechnology and Bioengineering, 1994. 43(8): p. 693-699.-   11. Mucida, D., et al., Reciprocal T<sub>H</sub>17 and Regulatory T    Cell Differentiation Mediated by Retinoic Acid. Science, 2007.    317(5835): p. 256.-   12. Agarwal, S. and A. Rao, Modulation of Chromatin Structure    Regulates Cytokine Gene Expression during T Cell Differentiation.    Immunity, 1998. 9(6): p. 765-775.-   13. Lai, Y., A. Asthana, and W. S. Kisaalita, Biomarkers for    simplifying HTS 3D cell culture platforms for drug discovery: the    case for cytokines. Drug Discovery Today, 2011. 16(7): p. 293-297.-   14. Coronel, A., et al., Cytokine production and T-cell activation    by macrophage-dendritic cells generated for therapeutic use. British    Journal of Haematology, 2001. 114(3): p. 671-680.-   15. Albrecht, S., et al., Proteomics in biomanufacturing control:    Protein dynamics of CHO-KI cells and conditioned media during    apoptosis and necrosis. Biotechnology and Bioengineering, 2018.    115(6): p. 1509-1520.-   16. Zhao, L., et al., Advances in process monitoring tools for cell    culture bioprocesses. Engineering in Life Sciences, 2015. 15(5): p.    459-468.-   17. Odeleye Akinlolu Oyekunle, O., et al., Development of an optical    system for the non-invasive tracking of stem cell growth on    microcarriers. Biotechnology and Bioengineering, 2017. 114(9): p.    2032-2042.-   18. Rice, W. L., D. L. Kaplan, and I. Georgakoudi, Two-Photon    Microscopy for Non-Invasive, Quantitative Monitoring of Stem Cell    Differentiation. PLOS ONE, 2010. 5(4): p. e10075.-   19. Teixeira, A. P., et al., Advances in on-line monitoring and    control of mammalian cell cultures: Supporting the PAT initiative.    Biotechnology Advances, 2009. 27(6): p. 726-732.-   20. Wang, M., et al., Microarray-based gene expression analysis as a    process characterization tool to establish comparability of complex    biological products: Scale-up of a whole-cell immunotherapy product.    Biotechnology and Bioengineering, 2009. 104(4): p. 796-808.-   21. Kirouac, D. C. and P. W. Zandstra, The Systematic Production of    Cells for Cell Therapies. Cell Stem Cell, 2008. 3(4): p. 369-381.-   22. Fenn, J. B., et al., ELECTROSPRAY IONIZATION FOR    MASS-SPECTROMETRY OF LARGE BIOMOLECULES. Science, 1989.    246(4926): p. 64-71.-   23. Vaughn Cecily, P., et al., Identification of proteins released    by follicular lymphoma-derived cells using a mass spectrometry-based    approach. PROTEOMICS, 2006. 6(10): p. 3223-3230.-   24. Jon Rowley, E. A., Andrew Campbell, Harvey Brandwein, Steve Oh,    Meeting Lot-Size Challenges ofManufacturing Adherent Cells for    Therapy. BioProcess Int, 2012. 10: p. 16-22.-   25. Nampe, D., et al., Impact of fluidic agitation on human    pluripotent stem cells in stirred suspension culture. Biotechnology    and Bioengineering, 2017. 114(9): p. 2109-2120.-   26. Rodrigues, C. A. V., et al., Stem cell cultivation in    bioreactors. Biotechnology Advances, 2011. 29(6): p. 815-829.-   27. Cech, N. B. and C. G. Enke, Practical implications of some    recent studies in electrospray ionization fundamentals. 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Example 2 Introduction

The Dynamic Sampling Platform with nano-electrospray ionization massspectrometry (DSP-nanoESI-MS, or DSP) is a device for the discovery andmonitoring of quality attribute (QAs) biomolecules (e.g. metabolites,proteins) secreted by therapeutic cells during their production. Thedevice samples cell media directly from the bioreactor and rapidlyconditions the sample for inline, real-time MS analysis (˜1 minute). TheDSP is combined with a spatially resolved sampling interface thatprovides non-invasive, sterile, and highly localized sampling from nearthe cell membrane to capture transient cell processes and to sample thehighest concentration of secreted biomolecules. The DSP-nanoESI-MStechnology, coupled with the novel sampling interface, has been appliedto multiple live cell cultures throughout the entire growth cycle (˜3weeks) without interfering with the cell growth trajectory. The recentresults demonstrate an ability to continuously sample from live cellcultures (MC3T3), provide MS “fingerprints” of cell state, and toexploit localized sampling with real-time MS analysis to detectdifferences in cells at different stages of cell growth (i.e.undifferentiated & differentiated cells). These results show that thetechnology does not interfere with cell culture sterility or affect cellgrowth processes, and importantly the device can enable the real-timedetection of secreted biochemicals that traditional on-line analytics donot detect.

Approaches/Methods:

DSP-nanoESI-MS is microfabricated in a cleanroom environment with fullymonolithic component integration to enable continuous collection,treatment, and direct infusion of ultra-small bioreactors samples fordynamic electrospray ionization mass spectrometry (ESI-MS) detection ofcell secretome. DSP-nanoESI-MS is comprised of three elements: 1) anon-invasive sampling interface, which has demonstrated the ability tocontinuously sample from 2D cell cultures without breaking sterility; 2)a microfabricated mass exchanger for sample treatment whichsimultaneously removes compounds not amenable to MS analysis, such asinorganic salts, and introduces compounds that enhance MS limit ofdetection; and, 3) an outlet for direct nanoESI-MS analysis, providingunambiguous identification/fingerprinting of biomolecules in cellcultures.

Results:

Recently published results have demonstrated that DSP is capable ofidentifying multiple relevant biomarkers in a chemically complexmixtures which served as a proxy to cell culture media.¹ The results ofthese experiments directed the optimization of DSP flow configurationsto enhance sample treatment effectiveness as well as sensitivity to lowconcentration biomolecules. With the pilot study complete, initialstudies on both 2D and 3D cell cultures began.

DSP has been applied to 2D (adherent) cell cultures to show that thetechnology is capable of differentiating between three adherent celltypes grown in media with FBS serum (human umbilical vein endothelialcells, human mesenchymal stromal cells, and normal human lungfibroblasts). Results indicate that DSP is able to distinguish thesecretome signature locally (i.e. near cells) vs bulk (from the media)for adherent fibroblast cells (unpublished data). Incorporation of DSPinto 3D cell cultures (MSC cells grown on microcarriers in spinningflask) has been demonstrated.

To understand the capability to detect differences between clinicallyrelevant cell groups, DSP was applied to MC3T3 cells. Two groups ofcells, one given differentiation media and one control, were monitoredover 18 days of cell growth. DSP measurements of both cell cultures weretaken every 3 days, with both local and bulk samples taken in triplicateat each time point. Principal component analysis was carried out toidentify differences between groups and revealed that localized samplingwas critical to detecting differences between undifferentiated anddifferentiated cell groups. An osteoblast cell group (MC3T3) wasselected for the pilot study due to the well characterized differencesin secretome for undifferentiated and differentiated cell groups, aswell as the robust nature of the cell line.²⁻⁴ The cells were dividedinto two groups for study. One group was given differentiation media,and the other given the control media used during initial cell expansionso that one cell line remained in an undifferentiated state throughoutthe entire study while the other progressed from an undifferentiated toa differentiated state. The cells were then analyzed every 3 days usingDSP coupled to direct nanoESI-MS analysis.

During sampling, a hot plate set to maintain the temperature of thecells at 37 C was placed under the 6-well plate to mitigate risk of celldeath. All sampling tubing (360 um OD, 50 um ID, PEEK) was autoclaved at100 C for 1 hour and kept in sterile packaging until sampling. A newtube was used every time to sample from the cell culture, with purgingusing sterile water carried out between each sample on the same cellculture. To allow contaminant free sampling, a sterile hypodermic needlewas used to puncture the aluminum foil, allowing for easy access of thePEEK tube into the cell culture. Immediately after sampling, the cellswere brought back to the cell culture lab and the media was changed perprotocol. During sampling, triplicate samples of 1 uL were taken fromthe bulk media above the cells and from a local region ˜50 um from thecell surface, as confirmed by a digital microscope angled horizontallyinto the 2D cell culture. This sample was routed directly through theDSP mass exchanger, where it was treated with 1% m-NBA and 1% AA in theconditioning channel (as per prior experiments). This resulted in six MSspectra for each cell group at each time point. In total, six timepoints were captured, resulting in a grand total of 72 spectra (36 bulk,36 local) for the cell groups, which enables robust statistical analysisand drawing meaningful conclusions from the data.

FIG. 3.1 shows representative spectra from the last time pointcollected. The top six spectra correspond to three bulk/local samplesfrom undifferentiated cells (normal media), and the bottom 6 correspondto bulk/local samples from differentiated cells (differentiation media).Although the two groups were established to be different through anassay to confirm differentiation, visually the spectra do not appear tohave any striking differences, except for the top spectra which islikely a technical outlier due to issues getting the DSP system set upinitially. Statistical analysis in the form of principal componentanalysis (PCA) was carried out to identify differences in the spectra.This analysis technique can reveal differences between the spectra thatcorrespond to candidate molecules or to increases/decreases in spectralintensities that correspond to cell state, i.e. “fingerprinting” thecells. For PCA analysis, a free software called “MultiMS-toolbox” wasutilized.⁵

Prior to PCA analysis, data was extracted from the Bruker software usingProteoWizard™ and then converted to the appropriate file format (mzML)for PCA analysis. Filtering was carried out on the raw data includingbaseline subtraction and peak smoothing. The signal to noise ratiothreshold of data was set to 0.5, and a Savitzky-Golay smoothing methodwas used to smooth noise that can distort the shape of the spectrum andskew final results. In order to maintain fidelity, PCA was run on theentire spectrum and then winnowed down according to raw loadings plots.The range of m/z values with the highest contribution to variance waschosen as the reduced window size. Without winnowing, the large amountof low intensity “noise” at the higher end of the spectra masked thecontribution of important features in the lower mass ranges.

For the first PCA comparison, spectra from time points 5 and 6 wereconsidered as one “group” for both the differentiated andundifferentiated cells for the first analysis. This analysis revealedthat localized sampling results in clustering of cells in adifferentiated and undifferentiated state. The cells in each culture aresecreting different molecules, and these molecules are at the highestconcentration near the cells themselves. Therefore, localized samplingcaptures a richer biochemical signal than bulk sampling does. FIGS.3.2A-3.2B show the PCA plots for the undifferentiated vs differentiatedcells. If the bulk data (FIG. 3.2A) are analyzed, no separation isevident between the groups. However, if only the localized sampling(FIG. 3.2B) is taken into consideration, separation and clusteringbetween the groups is observed, indicating that DSP is capable ofdetecting the differences between these differentiated andundifferentiated cell groups.

A second analysis was carried out on the differentiated cell line only.Although every sample was taken from the same cell culture, time points1 and 2 were grouped together to represent “undifferentiated” cellswhile time points 5 and 6 were grouped together to represent“differentiated cells”. FIGS. 3.3A-3.3B show the PCA plots, againrevealing that localized sampling (FIG. 3.3B) is the only methodrevealing strong clusters, confirming that this method of samplingenhances the ability of DSP to detect differences between cell groups.

The DSP is able to exploit localized sampling, inline “active” sampletreatment, and direct ESI-MS analysis to detect differences betweencells in undifferentiated and differentiated states. Offline analysis isunder way (via HPLC-MS/MS) to help identify candidate moleculescontributing most to the differences in the cell groups. Coupled with amass spectrometer capable of tandem mass spectrometry for MS/MSidentification of CQAs, DSP will serve as a discovery tool to identifypotential quality attribute molecules indicating cell state, and to thenmonitor these molecules online to control the trajectory of cells duringgrowth and differentiation. Ultimately, the new quality controlcapabilities afforded by DSP will enable the scale-up and scale-out ofcell manufacturing.

When DSP is used in conjunction with a bioreactor at a GMP facility,sampled and ionized analyte from the cell culture needs to betransferred to the mass spectrometer. The ion transfer capillary allowsfor the transfer of ions long distances with minimal loss to the inletof a mass spectrometer.⁶ The concept has been adapted for use with themass spectrometer in the lab and is shown in FIG. 3.4. Ions generated byESI are transferred to the MS inlet via vacuum drawn on a boxsurrounding the MS front end. The transfer tube and the vacuum tube arefed into a polycarbonate box via feedthrough pipe fittings. Both tubesare made of Tygon tubing, which does not interfere with MS analysis. Acustom extended MS inlet extends within the ion transfer tube toincrease the transfer efficiency of the system.

To test whether or not ions were efficiently transferred in this set-up,5 uM cytochrome-c with 1% AA was introduced to the ion transfer tubewhich was approximately 0.5 meters long. Without vacuum turned on, no MSsignal was observed. However, with vacuum on, the cytochrome-C signalwas completely recovered, as shown in FIG. 3.5 which shows directinfusion (top) versus ion transfer system (bottom). These resultsdemonstrate that DSP can be placed in close proximity to where the cellsare being grown, carry out analysis, and then transfer the analyte tothe mass spectrometer via ion transfer capabilities without sacrificingsensitivity to low concentration molecules.

EXAMPLE 2 REFERENCES

-   1. Chilmonczyk, M., et al., Dynamic Mass Spectrometry Probe (DMSP)    for ESI-MS Monitoring of Bioreactors for Therapeutic Cell    Manufacturing. Biotechnology and bioengineering, 2018.-   2. Murgia, A., et al., Potency biomarker signature genes from    multiparametric osteogenesis assays: will cGMP human bone marrow    mesenchymal stromal cells make bone? PloS one, 2016. 11(10): p.    e0163629.-   3. Siegel, G., et al., Phenotype, donor age and gender affect    function of human bone marrow-derived mesenchymal stromal cells. BMC    medicine, 2013. 11(1): p. 146.-   4. Galipeau, J., et al., International Society for Cellular Therapy    perspective on immune functional assays for mesenchymal stromal    cells as potency release criterion for advanced phase clinical    trials. Cytotherapy, 2016. 18(2): p. 151-159.-   5. Cejnar, P., et al., Principal component analysis of normalized    full spectrum mass spectrometry data in multiMS-toolbox: An    effective tool to identify important factors for classification of    different metabolic patterns and bacterial strains. Rapid    Communications in Mass Spectrometry, 2018. 32(11): p. 871-881.-   6. Garimella, S., et al., Gas-flow assisted ion transfer for mass    spectrometry. Journal of Mass Spectrometry, 2012. 47(2): p. 201-207.

Ratios, concentrations, amounts, and other numerical data may beexpressed in a range format. It is to be understood that such a rangeformat is used for convenience and brevity, and should be interpreted ina flexible manner to include not only the numerical values explicitlyrecited as the limits of the range, but also to include all theindividual numerical values or sub-ranges encompassed within that rangeas if each numerical value and sub-range is explicitly recited. Toillustrate, a concentration range of “about 0.1% to about 5%” should beinterpreted to include not only the explicitly recited concentration ofabout 0.1% to about 5%, but also include individual concentrations(e.g., 1%, 2%, 3%, and 4%) and the sub-ranges (e.g., 0.5%, 1.1%, 2.2%,3.3%, and 4.4%) within the indicated range. In an embodiment, the term“about” can include traditional rounding according to significant figureof the numerical value. In addition, the phrase “about ‘x’ to ‘y’”includes “about ‘x’ to about ‘y’”.

Unless defined otherwise, all technical and scientific terms used havethe same meaning as commonly understood by one of ordinary skill in theart to which this disclosure belongs. Although any methods and materialssimilar or equivalent to those described can also be used in thepractice or testing of the present disclosure, the preferred methods andmaterials are now described.

Embodiments of the present disclosure will employ, unless otherwiseindicated, techniques of separating, testing, and constructingmaterials, which are within the skill of the art. Such techniques areexplained fully in the literature.

It should be emphasized that the above-described embodiments are merelyexamples of possible implementations. Many variations and modificationsmay be made to the above-described embodiments without departing fromthe principles of the present disclosure. All such modifications andvariations are intended to be included herein within the scope of thisdisclosure and protected by the following claims.

1-53. (canceled)
 54. A system comprising: a flow system including a pumpsystem and a valve system, wherein the pump system and the valve systemare in fluidic communication; a sampling system in fluidic communicationwith the flow system; a reactor comprising a fluid, wherein the samplingsystem is in fluidic communication with the reactor, wherein in a firstconfiguration of the flow system the fluid sample is flowed from thereactor to the pump system through the valve system at a first flowrate; and a sensor system in fluidic communication with the flow system,wherein in a second configuration of the valve system the fluid sampleis flowed from the pump system to the sensor system at a second flowrate, wherein the sensor system is configured to analyze the fluidsample.
 55. The system of claim 54, further comprising a feedbackcontrol system configured to use the output of the sensor system tomodify conditions of the reactor, wherein the system is a closed-loopfeedback system.
 56. The system of claim 54, wherein the first flow rateand the second flow rate are different.
 57. The system of claim 54,wherein the sample system comprises an extraction element that isdimensionally configured to extract the fluid sample comprisingsecretome, metabolome, transcriptome, genome, lipidome, or combinationsthereof and excluding an intact cell, a microcarrier including one ormore intact cells, or combinations thereof.
 58. The system of claim 54,wherein the sample system comprises an extraction element that isdimensionally configured to extract the fluid sample comprising amicrocarrier including one or more intact cells, wherein the extractionelement is further dimensionally configured to trap the microcarrier.60. The system of claim 54, wherein the sample system is configured tosequentially or simultaneously obtain fluid samples from one or moreregions of the reactor in the x-, y-, and z-axis.
 61. The system ofclaim 54, further comprising a separation/fractionation/trapping systemin fluidic communication with the flow system and the sampling system,wherein the separation/fractionation/trapping system is configured toseparate a first group of components from the fluid sample to produce aseparated/fractionated fluid sample, wherein the separated/fractionatedfluid sample is analyzed by the sensor system.
 62. The system of claim54, further comprising a separation system in fluidic communication withthe flow system and the sensor system, wherein the separation system isconfigured to separate a first group of components from the fluid sampleto produce a separated fluid sample, wherein the separated fluid sampleis analyzed by the sensor system.
 63. The system of claim 54, furthercomprising a mass exchanger in fluidic communication with the flowsystem and the sampling system, wherein the mass exchanger is configuredto condition the fluid sample to produce a conditioned fluid sample foranalysis by the sensor system, wherein the conditioned fluid sample isflowed through the mass exchanger.
 64. The system of claim 54, furthercomprising a mass exchanger in fluidic communication with the flowsystem and the sensor system, wherein the mass exchanger is configuredto condition the fluid sample to produce a conditioned fluid sample foranalysis by the sensor system or a mass exchanger in fluidiccommunication with the flow system and the separation system, whereinthe mass exchanger is configured to condition the separated fluid sampleto produce a conditioned fluid sample for analysis by the sensor systemor a mass exchanger in fluidic communication with the separation systemand the sensor system, wherein the mass exchanger is configured tocondition the separated fluid sample to produce a conditioned fluidsample for analysis by the sensor system or all of these.
 65. The systemof claim 64, wherein the mass exchanger includes a first flow channelhaving a first flow channel entrance and a first flow channel exit, anda second flow channel having a second flow channel entrance and a secondflow channel exit, where the first flow channel and the second flowchannel are separated from one another by a selectively permeablemembrane.
 66. The system of claim 65, wherein the mass exchanger isconfigured to flow the fluid sample flow through the first flow channelfrom the first flow channel entrance to the first flow channel exit andbe in fluid communication with the selectively permeable membrane,wherein the mass exchanger is configured to flow a conditioning fluidthrough the second flow channel from the second flow channel entranceand the second flow channel exit and be in fluid communication with theselectively permeable membrane, wherein the sample fluid and theconditioning fluid are in communication through the selectivelypermeable membrane.
 67. The system of claim 66, wherein the massexchanger includes a first flow channel having a first flow channelentrance and a first flow channel exit, a second flow channel having asecond flow channel entrance and a second flow channel exit, and a thirdflow channel having a third flow channel entrance and a third flowchannel exit, where the first flow channel and the second flow channelare separated from one another by a first selectively permeablemembrane, and where the third flow channel and the second flow channelare separated from one another by a second selectively permeablemembrane, wherein the mass exchanger is configured to flow the fluidsample through the second channel, wherein the mass exchanger isconfigured to flow a first conditioning fluid through the first flowchannel, wherein the mass exchanger is configured to flow a secondconditioning fluid through the third flow channel.
 68. The system ofclaim 66, wherein the mass exchanger including the first flow channelhaving the first flow channel entrance and the first flow channel exit,wherein the first flow channel further comprises a trapping chamber,wherein the trapping chamber is dimensionally configured to trap one ormore microcarriers or cells, wherein the first flow channel isconfigured to lyse the cells of the microcarriers or the cells, whereinthe components of the cytoplasm are the sample fluid and flow throughthe first flow channel and are in fluidic communication with theselectively permeable membrane.
 69. The system of claim 66, wherein themass exchanger includes a first flow channel having a first flow channelentrance and a first flow channel exit, a second flow channel having asecond flow channel entrance and a second flow channel exit, and a thirdflow channel having a third flow channel entrance and a third flowchannel exit, where the first flow channel and the second flow channelare separated from one another by a first selectively permeablemembrane, and where the third flow channel and the second flow channelare separated from one another by a second selectively permeablemembrane, wherein the mass exchanger is configured to flow a firstconditioning fluid through the first flow channel, wherein the massexchanger is configured to flow a second conditioning fluid through thethird flow channel, wherein the second flow channel further comprises atrapping chamber, wherein the trapping chamber is dimensionallyconfigured to trap one or more microcarriers or cells, wherein thesecond flow channel is configured to lyse the cells of the microcarriersor the cells, wherein the components of the cytoplasm are the samplefluid and flow through the second flow channel and are in fluidiccommunication with the first selectively permeable membrane and thesecond selectively permeable membrane.
 70. The system of claim 66,wherein the mass exchanger configured to lyse includes electroporation,chemical digestion, mechanoporation, sonoporation, and thermoporation,osmotic stressing, or a combination thereof.
 71. The system of claim 70,wherein the mass exchanger is configured so that the sample fluid isconverted to a conditioned fluid sample through interaction of thesample fluid with the conditioning fluid(s), the selectively permeablemembrane(s), or a combination thereof, wherein the sensor systemanalyzes the conditioned fluid sample.
 72. The system of claim 66,wherein the sensor system comprises one or more identical or distinctsensing devices.
 73. The system of claim 72, wherein the sensor systemis configured to operation so that the fluid sample is analyzed inparallel by two or more sensing devices or wherein the sensor system isconfigured to operation so that the fluid sample is analyzed serially bytwo or more sensing devices.
 74. The system of claim 72, wherein thesensor system comprises a sensing device selected from: ESI-MS, RamanSpectrometer, FTIR Spectrometer, UV-VIS Spectrometer, ESEM/SEM, OpticalMicroscope, Fluorescence Microscope, NMR, Electrochemical Redox and/orImpedance Sensor, Flow Cytometer, and Acoustic Transducer.